ThAct: Documentation - Preparing a List of Works Cited

chapter 4  ThAct: Documentation - Preparing a List of Works Cited

Long question: 

1. Difference Between Bibliography and Citation

Introduction

In academic research and scholarly writing, giving proper credit to sources is extremely important. Whenever students or researchers use ideas, facts, statistics, or direct quotations from other writers, they must acknowledge those sources appropriately. This practice not only helps in avoiding plagiarism but also increases the reliability and authenticity of the research work. Two essential components used for acknowledging sources are citations and bibliographies. Although these terms are related and often appear together in academic papers, they are not identical. They serve distinct purposes and are placed in different sections of a research document. Therefore, understanding the difference between citation and bibliography is necessary for maintaining academic honesty and producing well-organized research.

What Is a Citation?

A citation is a short reference that appears within the main body of a research paper. It indicates the origin of specific information, ideas, or quotations that the writer has borrowed from another source. Whenever a writer quotes directly, paraphrases, or summarizes someone else's work, a citation must be included. Depending on the referencing style being followed such as MLA, APA, or Chicago a citation may appear in parentheses within the text, as a footnote at the bottom of the page, or as an endnote at the end of the chapter.

The main function of a citation is to clearly show readers where a particular piece of information originated. It generally includes brief details such as the author’s name, year of publication, and page number. These details guide readers to the full reference listed in the works cited or reference section. By providing citations, writers separate their original ideas from borrowed material, thereby preventing plagiarism. Citations also allow readers to confirm facts, evaluate the credibility of the information, and consult the source for further study.

For instance, if a student inserts a direct quotation from a textbook, a citation must immediately follow the quotation to identify the author and page number. Without such acknowledgment, the act would be considered plagiarism.

What Is a Bibliography?

A bibliography is a complete list of all the sources that a writer has used or consulted while preparing a research paper. It is placed at the end of the document on a separate page. Unlike citations, which are brief and appear within the text, bibliography entries contain full publication information.

A typical bibliography entry includes the author’s complete name, the full title of the book or article, the publisher’s name, the place of publication, and the year of publication. Depending on the citation style, additional information such as edition details, page ranges, URLs, or DOIs may also be included. The bibliography demonstrates the range and depth of research undertaken by the writer. It reflects the effort made to consult credible and scholarly materials.

In certain cases, a bibliography may include all sources that were consulted during research even those that were not directly quoted or cited in the paper. This makes it different from a “Works Cited” or “Reference List,” which generally contains only the sources that are directly mentioned in the text.

Thus, while citations identify specific borrowed content within the paper, the bibliography provides a comprehensive record of research sources.

Differences Between Citation and Bibliography

1. Difference in Placement

One of the most noticeable differences between citations and bibliographies is where they appear in a research paper. Citations are included within the body of the text. They may appear in parentheses, footnotes, or endnotes depending on the referencing style. Because they are placed directly after borrowed information, they immediately inform the reader about the source.

On the other hand, a bibliography is located at the end of the document. It is usually arranged alphabetically according to the authors’ last names. Readers must refer to this final section to view the complete details of all sources used. Therefore, citations are integrated into the discussion, whereas bibliographies are compiled in a separate concluding section.

2. Difference in Level of Detail

Citations are concise and provide only essential information. They typically include the author’s surname, publication year, and page number. Their purpose is to identify the source quickly without disturbing the flow of the writing.

In contrast, a bibliography contains detailed and complete information about each source. It provides the author’s full name, complete title, publisher, year, place of publication, and sometimes additional identifiers like URLs or DOIs. This level of detail enables readers to locate the exact source independently. Hence, citations are brief references, while bibliographies are thorough descriptions.

3. Difference in Purpose

The central aim of a citation is to acknowledge the original author and avoid plagiarism. It makes clear which ideas belong to other writers and which are the author’s own contributions. Without citations, borrowed material may appear as original writing, leading to academic misconduct.

A bibliography, however, serves a broader purpose. It showcases the overall research effort and demonstrates that the writer has relied on trustworthy and relevant sources. It adds credibility to the paper by revealing the scholarly foundation behind the work. While citations focus on crediting specific information, bibliographies highlight the scope and seriousness of the research.

4. Difference in Scope

Citations are limited to sources that are directly quoted, paraphrased, or summarized within the text. If a source is not specifically used in the writing, it may not appear as a citation.

A bibliography may include both cited sources and additional materials that were consulted for background understanding. For example, a student may read multiple books while preparing a paper but use only a few for direct quotations. In such a case, all consulted books might appear in the bibliography, even though only some are cited in the text. Therefore, bibliographies often cover a wider range of sources than citations.

5. Difference in Usefulness for Readers

From a reader’s perspective, citations provide immediate evidence that a statement is supported by research. They allow readers to quickly identify the origin of specific information and verify its authenticity.

A bibliography, on the other hand, acts as a valuable resource for further reading. It offers a complete list of materials related to the topic, which readers can consult if they wish to explore the subject more deeply. In this way, citations assist in source identification, while bibliographies promote extended learning.

Conclusion

To conclude, citation and bibliography are related but distinct elements of academic writing. A citation is a short reference placed within the text to acknowledge the source of specific borrowed information. A bibliography is a detailed list of all sources consulted during the research process and appears at the end of the paper. Together, they ensure transparency, accuracy, and academic integrity in scholarly writing. A clear understanding of their differences helps students and researchers produce ethical, credible, and well-structured research work.

Short question 

2. Citation

Introduction

In academic research and formal writing, supporting ideas with trustworthy sources is essential. Scholars and students often rely on books, journals, articles, and other materials to strengthen their arguments. However, whenever information, opinions, statistics, or exact words are taken from another author, proper acknowledgment must be given. This acknowledgment is called citation. Citation is a core principle of academic writing because it promotes fairness, clarity, and respect for the intellectual efforts of others.

Meaning and Explanation

A citation is a formal reference that identifies the source of particular information used in an academic paper. It informs readers about where a specific idea, quotation, or piece of evidence originally appeared. Citations may be presented within the body of the text (known as in-text citations) or placed as footnotes or endnotes, depending on the citation style required, such as MLA, APA, or Chicago style.

Typically, a citation contains short but important details, including the author’s name, publication year, and sometimes the page number. These brief details guide readers to the complete source information provided in the bibliography, reference list, or works cited section at the end of the document. By offering this clear connection, citations allow readers to check the accuracy of the information and consult the original material if needed.

It is important to note that citations are required whenever a writer directly quotes someone, paraphrases an author’s viewpoint, summarizes research, or includes specific data that is not considered common knowledge. Even when ideas are rewritten in new words, they must still be credited to the original creator because the concept itself belongs to that source.

Importance of Citation

Citation is crucial in preventing plagiarism, which occurs when a person presents another’s work as their own. By properly acknowledging sources, writers demonstrate honesty and uphold academic integrity. Citations also increase the strength and reliability of research by showing that arguments are supported by established evidence rather than personal opinion alone.

Furthermore, citation acknowledges the work of other researchers and scholars. Academic knowledge develops over time through continuous discussion and contribution. By citing previous work, writers become part of this larger scholarly dialogue, building upon existing ideas while giving recognition to those who contributed before them.

Conclusion

To conclude, citation is a vital component of academic writing that involves clearly identifying the sources of borrowed information. It ensures ethical writing practices, helps avoid plagiarism, and improves the credibility of research. Proper citation reflects respect for intellectual property and supports the principles of transparency and responsibility in scholarly work. Therefore, citation is not merely a formal rule but an essential duty of every academic writer.

Annotated Bibliography

Topic: Climate Change

The following eight sources represent a range of source types journal article, book, news article, encyclopedia entry, book chapter, webpage, documentary film, and satellite image all pertaining to the topic of climate change. Citations follow MLA 9th edition format.

1. Journal Article

Oreskes, Naomi. "The Scientific Consensus on Climate Change." Science, vol. 306, no. 5702, 3 Dec. 2004, p. 1686. JSTOR, https://doi.org/10.1126/science.1103618.

In this influential brief report, Oreskes reviewed 928 peer-reviewed article abstracts on climate change published between 1993 and 2003. Her analysis revealed that none of the abstracts directly challenged the prevailing scientific view that human activities significantly contribute to global warming. The article has become a key reference point for discussions about the level of agreement within the scientific community regarding anthropogenic climate change. It continues to be widely cited in debates related to science policy and public understanding, and its research approach has shaped many later studies examining scientific consensus.

2. Book

Wallace-Wells, David. The Uninhabitable Earth: Life After Warming. Tim Duggan Books, 2019.

Expanding on his widely discussed 2017 New York Magazine article, Wallace-Wells compiles scientific forecasts and research to present a detailed exploration of the potential consequences of global warming. Through thematic chapters, he examines how increasing temperatures may disrupt agriculture, reduce freshwater resources, intensify health crises, strain economic systems, and heighten political instability worldwide. Although written for a broad readership, the book maintains strong grounding in scientific evidence and research. Wallace-Wells emphasizes that even the most severe climate projections should be taken seriously in public and policy discussions. As a result, the book has become a commonly recommended text in university programs focused on environmental studies and climate communication.

3. News Article

Davenport, Coral. "Major Climate Report Describes a Strong Risk of Crisis as Early as 2040." The New York Times, 7 Oct. 2018, www.nytimes.com/2018/10/07/climate/ipcc-climate-report-2040.html.

This article covers the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Global Warming of 1.5°C, published in October 2018. In clear and accessible language, Davenport summarizes the report’s central warning: unless swift and far-reaching global action is taken, severe climate consequences could emerge as early as 2040. The article not only explains the scientific findings but also places them within the context of political discussions and policy debates in the United States. By connecting complex scientific research with real-world political responses, the piece serves as an important example of how technical climate data is communicated to the general public and integrated into national policy conversations.

4. Encyclopedia Entry

"Climate Change." Encyclopædia Britannica, Encyclopædia Britannica, Inc., 2024, www.britannica.com/science/climate-change.

This detailed entry from Encyclopædia Britannica offers a well-structured and reliable introduction to the topic of climate change. It explains the concept clearly, outlining both natural factors and human activities that contribute to climatic shifts. The article also discusses historical patterns of climate variation, documented environmental impacts, and predictions for future global conditions. Important scientific ideas such as the greenhouse effect, carbon cycle feedback mechanisms, and ocean acidification are presented in an accessible manner. By distinguishing between short-term weather changes and long-term climate trends, the entry helps readers build a foundational understanding of the subject. Because it is regularly reviewed and updated, it serves as a trustworthy starting reference for students and researchers beginning their study of climate change.

5. Book Chapter

Norgaard, Kari Marie. "Climate Denial: Emotion, Psychology, Culture, and Political Economy." The Oxford Handbook of Climate Change and Society, edited by John S. Dryzek, Richard B. Norgaard, and David Schlosberg, Oxford University Press, 2011, pp. 399–413.

In this chapter, Norgaard challenges the common assumption that climate skepticism is simply the result of a lack of knowledge. Instead, she argues that denial often functions as a socially structured and emotionally motivated reaction to unsettling scientific information. Based on her sociological research conducted in a Norwegian community, she explores how shared cultural values, national identity, and collective emotional responses influence whether people confront or ignore climate change evidence. By examining the interaction between psychology, culture, and political economy, Norgaard provides a deeper understanding of why even highly educated and scientifically aware societies may struggle to respond effectively to clear environmental warnings.

6. Webpage

NASA. "Climate Change: How Do We Know?" NASA Global Climate Change, NASA Jet Propulsion Laboratory / California Institute of Technology, 2024, climate.nasa.gov/evidence.

This webpage from NASA outlines the wide range of scientific evidence demonstrating that climate change is largely driven by human activities. It brings together various independent data sources, including recorded global temperature trends, satellite observations, measurements of rising sea levels, shrinking ice sheets, and ice-core records that reveal past atmospheric conditions. Created for educational purposes, the page explains complex scientific findings in straightforward language and supports them with clear visuals and graphics. It also provides links to original datasets and peer-reviewed research for those seeking more detailed information. Managed and regularly updated by NASA’s Jet Propulsion Laboratory, the site is considered one of the most credible and authoritative online resources for evidence related to climate change.

7. Documentary / Video

Gore, Al. An Inconvenient Truth. Directed by Davis Guggenheim, performances by Al Gore, Paramount Classics, 2006.

This Academy Award–winning documentary centers on former U.S. Vice President Al Gore’s presentation about the scientific evidence and potential consequences of global warming. Directed by Davis Guggenheim, the film transforms complex climate data—such as melting glaciers, rising sea levels, and increasing extreme weather events—into powerful and engaging visual narratives that are accessible to a broad audience. Released in the mid-2000s, it played a major role in bringing climate change into mainstream public discussion. Although certain predictions have later been updated or refined by ongoing research, the documentary remains an important milestone in climate awareness and communication, inspiring educational programs and global environmental activism.

8. Image / Visual Source

NASA Earth Observatory. Arctic Sea Ice Minimum 1984 vs. 2016 [Satellite composite image]. NASA / GSFC / SVS, 2016, earthobservatory.nasa.gov/images/88535/arctic-sea-ice-minimum-extents.

This set of satellite composite images created by NASA’s Goddard Space Flight Center visually compares the lowest Arctic sea-ice coverage recorded in the summers of 1984 and 2016. By placing the two years side by side, the images clearly demonstrate the significant decline in sea ice over more than three decades. What might otherwise seem like abstract climate statistics becomes immediately understandable through the visible shrinking of the white ice area. The comparison highlights the impact of Arctic amplification and serves as striking visual proof of ongoing environmental change. Frequently featured in textbooks, media reports, and policy discussions, the image has become an influential resource for illustrating the realities of climate change and enhancing public understanding.

Study the introductory section of that article and identify whether the section adheres to one or more of the 7 principles of inclusive language as discussed by the 9th edition of the MLA Handbook. Justify your observations. 

For this  i chose the article : "Refugee Status Determination in Brazil: Enacting Injustice"

Click here for article :"Refugee Status Determination in Brazil: Enacting Injustice"

Inclusive Language in the Introduction of "Refugee Status Determination in Brazil: Enacting Injustice"

The opening section of Flávia Rodrigues de Castro’s article reflects a thoughtful engagement with the principles of inclusive language outlined in the MLA Handbook (9th ed.). A close reading suggests that the introduction aligns sometimes explicitly and sometimes implicitly with at least four of the seven inclusive language principles identified by MLA. Her linguistic and conceptual choices demonstrate a conscious effort to avoid reduction, stereotyping, and exclusion.

Principle 3: Choose Terms of Identity That Respect Your Subject

Among the MLA principles, Principle 3 is most clearly and consistently reflected in Castro’s introduction. Rather than defining individuals solely by legal labels such as “refugees” or using stigmatizing terminology like “bogus applicants,” she adopts people-centered and process-oriented language. Terms such as “asylum seekers,” “applicants,” and “individuals” foreground personhood before legal classification. Even when discussing the institutional framework, the phrasing emphasizes action and context “those seeking protection” instead of fixed identity categories.

More importantly, Castro challenges the assumption that “refugeehood” is an inherent or essential characteristic. Her assertion that “Refugeehood is not an ontological condition established a priori” directly critiques the idea that individuals possess a predetermined refugee identity. By resisting essentialist definitions, she aligns with Principle 3’s guidance against reducing people to a single defining attribute. The refusal to treat “refugee” as a static identity category is itself an enactment of inclusive thinking at the conceptual level.

Principle 5: Minimize Pronouns That Exclude

Another significant stylistic decision in the introduction is Castro’s consistent use of the feminine pronoun “she” when referring to a generic or hypothetical asylum seeker. For example, she writes, “a person is a refugee because she is recognised as such.” Similarly, when discussing theoretical frameworks such as Fricker’s epistemology, the generic subject is feminized rather than defaulting to “he.”

According to Principle 5 of the MLA Handbook (9th ed.), one acceptable strategy for avoiding exclusionary language is the consistent use of feminine pronouns instead of the traditional masculine generic. Although singular “they” is now widely preferred as the most neutral option, Castro’s deliberate choice to employ “she” disrupts the long-standing male default in academic discourse. Given that many asylum seekers are women or gender-diverse individuals whose experiences are often marginalized in legal systems, this linguistic shift carries symbolic and political significance beyond mere stylistic variation.

Principle 2: Be Precise

Castro’s introduction also demonstrates strong adherence to Principle 2, which emphasizes clarity and precision when referring to social groups and institutions. She avoids generalizing language that would treat “refugees” as a uniform or homogeneous group. Instead, she carefully distinguishes between “examiners,” “civil society actors,” “applicants,” and “refugees,” thereby acknowledging the differentiated roles and perspectives within the asylum system.

Institutional references are equally precise. Rather than vaguely referring to “authorities” or “international agencies,” Castro specifically names CONARE, UNHCR, and Brazil’s tripartite asylum structure. This attention to institutional detail strengthens the credibility of her analysis. Notably, precision in her own language mirrors one of her central critiques: that the Refugee Status Determination (RSD) system often fails to treat applicants with the nuance and specificity they deserve. By modeling careful differentiation, she enacts the analytical rigor she expects from the institutions she examines.

Principle 6: Avoid Negatively Judging Others’ Experiences

Principle 6 advises writers to avoid framing marginalized individuals in language that emphasizes victimhood or suffering in reductive ways. Castro’s introduction reflects this guidance by presenting asylum seekers not as passive victims but as epistemic agents. Rather than describing them as people who “suffer” or are “afflicted,” she conceptualizes them as “subjects of knowledge.”

This formulation is central to her argument. The injustice she identifies lies not merely in material hardship but in epistemic marginalization the denial of credibility and recognition within institutional decision-making processes. By focusing on their capacity to know, testify, and interpret their own experiences, Castro frames asylum seekers with dignity and intellectual agency. This approach avoids pity-based language and instead foregrounds respect and analytical depth.

A Brief Note on Principle 1: Make References to Identity Relevant

The introduction also engages, though more subtly, with Principle 1. Castro does not unnecessarily foreground markers such as race, nationality, or gender unless they are analytically essential. When identity categories are referenced, they are treated as objects of critique rather than as descriptive labels. Her broader argument that nationality and ethnicity can improperly influence credibility assessments requires these identity markers to be examined critically.

In this way, identity references are not decorative or gratuitous; they are integral to the argument. This selective and purposeful use of identity language reflects the spirit of Principle 1: references to identity should be included when relevant and meaningful to the discussion. In Castro’s work, they are not peripheral but structurally significant.

Conclusion

Overall, the introduction of “Refugee Status Determination in Brazil: Enacting Injustice” demonstrates a thoughtful and deliberate application of inclusive language principles consistent with the MLA Handbook (9th ed.). Through careful word choice, pronoun usage, conceptual framing, and analytical precision, Castro avoids reductive labels and exclusionary norms. Her language choices do not merely comply with stylistic guidelines; they reinforce her broader critique of institutional injustice. In doing so, she shows that inclusive language is not only an ethical practice but also a methodological and philosophical stance embedded within scholarly argumentation.

References :

Modern Language Association of America. MLA Handbook. 9th ed., Modern Language Association of America, 2021.

Thank you.

ThAct: Plagiarism and Academic Integrity

  

Chapter 2: Plagiarism and Academic Integrity

This assignment was given by Prakruti Ma’am for the Research Methodology course, focusing on Chapter 2, which deals with Plagiarism and Academic Integrity. We were required to read the chapter thoroughly, make comprehensive notes, and then explain the concepts in our own language. In addition, we had to answer selected questions from the syllabus question bank in both short and long answer formats. The main purpose of this task is to ensure that we clearly understand the core ideas of the chapter and can critically interpret and present them effectively in our writing.

Long Question:

1. Why Is Academic Integrity Necessary?

Introduction

Academic integrity is essential to the true purpose of education. Education is not simply about obtaining marks, completing coursework, or receiving certificates; it is about developing knowledge, critical thinking skills, and ethical values. Academic institutions function effectively only when students and scholars follow principles of honesty and responsibility. Without integrity, the value of education would decline, and academic achievements would lose credibility. In the modern digital age, where information is easily accessible and copying content is effortless, maintaining honesty in academic work has become more important than ever. Academic integrity ensures that learning remains genuine and that success reflects real understanding and effort.

Meaning of Academic Integrity

Academic integrity refers to maintaining honesty, fairness, and accountability in all academic tasks. It requires students and researchers to create original work, properly cite sources, and avoid dishonest practices such as plagiarism, cheating, falsifying information, or unauthorized cooperation. More than just following institutional rules, academic integrity involves personal ethics and self-discipline. It means clearly separating one’s own ideas from borrowed ideas and acknowledging the contributions of others. In simple terms, academic integrity ensures that academic work truthfully represents a student’s own knowledge and effort.

Building Trust Within the Academic Environment

One major reason academic integrity is necessary is that it creates trust among members of the academic community. Teachers trust students to submit authentic work, and students trust teachers to evaluate fairly. Researchers depend on the accuracy of previous studies to conduct further research. When dishonesty becomes common, this trust is damaged. Suspicion replaces confidence, and the academic environment becomes unhealthy. Academic integrity maintains transparency and reliability, allowing knowledge to grow in a respectful and trustworthy atmosphere.

Protecting Intellectual Effort and Creativity

Academic integrity safeguards intellectual property and originality. Every academic work represents significant time, research, and intellectual contribution. Copying someone’s ideas without proper acknowledgment is a form of intellectual theft. By citing sources and respecting authorship, students honor the creative efforts of others. At the same time, integrity encourages individuals to think independently and develop their own viewpoints. Education values originality, and academic integrity ensures that creativity and innovation are properly recognized.

Encouraging True Learning and Skill Development

The goal of education is intellectual growth. When students complete assignments honestly, they develop research skills, critical thinking abilities, analytical reasoning, and effective communication. Dishonest shortcuts may provide temporary academic gains but prevent long-term development. For instance, writing assignments help students learn how to organize ideas and evaluate evidence. Avoiding these tasks through cheating stops meaningful learning. Academic integrity ensures that students actively engage in their studies and gain lasting knowledge.

Maintaining Institutional Reputation

The reputation of educational institutions depends largely on their academic standards. Degrees symbolize competence and knowledge. If students earn qualifications dishonestly, the credibility of the institution suffers. Society expects graduates to be skilled and ethical professionals. Widespread academic misconduct can weaken public confidence in education systems. Therefore, academic integrity is crucial for maintaining institutional honor and social trust.

Ensuring Fairness and Equal Opportunity

Academic integrity guarantees fairness among students. Honest students should not be disadvantaged by those who choose dishonest methods. When cheating occurs, it creates inequality and reduces motivation among hardworking individuals. Integrity ensures that success is based on merit and effort. This fairness strengthens confidence in the evaluation process and encourages healthy academic competition.

Preparing for Ethical Professional Conduct

The values learned during academic life influence future professional behavior. Integrity in education builds responsibility, accountability, and moral judgment. In professional settings, dishonesty can lead to serious consequences such as legal issues, job termination, and loss of reputation. Practicing academic honesty prepares students to behave ethically in their careers and contribute positively to society.

Supporting Personal Development and Self-Respect

Completing work honestly provides a sense of pride and self-worth. Achievements gained through genuine effort increase confidence and self-respect. In contrast, dishonesty often brings guilt and insecurity. Academic integrity helps individuals understand their strengths and weaknesses and promotes personal growth. It shapes strong moral character that extends beyond academic life.

Avoiding Academic and Legal Penalties

Academic misconduct can result in serious punishments, including failure, suspension, expulsion, or permanent academic records. In certain cases, plagiarism may also involve legal consequences related to copyright laws. By maintaining integrity, students protect their academic futures and avoid long-term negative outcomes.

Conclusion

In summary, academic integrity is necessary because it strengthens trust, protects intellectual ownership, promotes real learning, preserves institutional reputation, ensures fairness, encourages ethical professionalism, supports personal growth, and prevents serious consequences. It is not merely a formal rule but a guiding principle that upholds the value of education. Without honesty and responsibility, academic success loses its true meaning. Therefore, academic integrity should be viewed as both an academic obligation and a lifelong ethical commitment.

Short Question: 

2. Issues Related to Plagiarism

Introduction

Plagiarism is more than simply copying someone else’s words without giving credit. It is a serious ethical concern that affects honesty, trust, and credibility in academic and professional environments. When plagiarism occurs, it weakens the foundation of academic integrity and creates unfair advantages. In addition to direct copying, there are several related concerns that make plagiarism a complex issue. Understanding these challenges is important for students and researchers who wish to maintain ethical standards in writing and research.

Major Issues Connected to Plagiarism

1. Self-Plagiarism (Recycling One’s Own Work)

Self-plagiarism happens when a student reuses their previously submitted assignment or research paper for another course without permission. Even though the work originally belongs to the student, presenting it again as new work is misleading. Academic institutions expect fresh effort and new learning in each assignment. Reusing earlier work without approval misrepresents originality and limits intellectual growth. Therefore, self-plagiarism is treated as a violation of academic integrity.

2. Accidental or Unintentional Plagiarism

Plagiarism does not always occur deliberately. In many cases, students unintentionally copy material because of weak note-taking practices, incorrect paraphrasing, or missing citations. They may forget to include quotation marks or unintentionally follow the original author’s sentence structure too closely. Even without harmful intent, such mistakes are still considered plagiarism. This highlights the importance of learning proper citation methods and carefully reviewing academic work.

3. Improper Collaboration

Group work and teamwork are common in academic settings and often encouraged. However, issues arise when students collaborate beyond permitted limits or fail to clearly identify shared contributions. Submitting jointly completed work as individual effort can be misleading. To avoid plagiarism in collaborative tasks, students must follow instructor guidelines and clearly acknowledge each person’s contribution.

4. Copyright Violations

Plagiarism and copyright infringement are related but not identical issues. Plagiarism is mainly an ethical problem involving failure to give credit, while copyright infringement is a legal matter concerning unauthorized use of protected material. Even if a source is properly cited, copying large sections without permission may violate copyright laws. Therefore, students must be careful not only about citation but also about respecting legal restrictions on published content.

5. Ethical Concerns in Human Research

In research involving human participants, ethical responsibilities extend beyond avoiding textual plagiarism. Researchers must obtain informed consent, protect participant privacy, and follow institutional regulations. Ignoring these ethical requirements is considered academic misconduct. Responsible research practices are essential for maintaining trust and upholding integrity within the scholarly community.

Conclusion

Plagiarism is a broad and multifaceted issue that goes beyond copying text. It includes self-plagiarism, unintentional mistakes, misuse of collaboration, copyright violations, and unethical research behavior. Recognizing these related concerns helps students and researchers act responsibly and maintain academic honesty. By understanding and addressing these issues, individuals can strengthen academic integrity and contribute ethically to the world of research and scholarship.

Respond to the following ethical dilemma prompts:

• A student rewrites a scholarly paragraph by changing sentence structure and vocabulary but retains the same ideas and sequence of argument. They do not provide a citation because they believe they are “not copying anything.” 

How should this be treated under MLA guidelines? Does paraphrasing require citation? What would you do in this situation and why?

answer:

According to MLA guidelines, even if a student rewrites a passage in their own words or changes the sentence structure, it is still considered the use of another person’s ideas. MLA format requires citation not only for direct quotations but also for paraphrased or summarized content. When the main ideas, reasoning, or sequence of arguments are borrowed from a source, the intellectual ownership still belongs to the original author. Therefore, paraphrased material must also be properly cited.

In this case, I would consider it unintentional plagiarism. I would clarify to the student that plagiarism does not only mean copying someone’s exact wording; it also involves presenting another author’s ideas without acknowledgment. I would advise them to include the correct in-text citation and list the source on the Works Cited page to correct the mistake.

I would respond this way because academic integrity is grounded in honesty and proper acknowledgment of sources. Even if the student had no intention to cheat, citing sources is essential to show respect for the original author’s work and to maintain ethical standards in academic writing.

• Two classmates study together, exchange notes, and discuss how to approach an essay. Their final essays are not identical in wording but share the same structure, examples, and argument path. 

Is this plagiarism, collaboration, or something in between? How should credit or boundaries operate?

This case falls somewhere between acceptable collaboration and plagiarism, depending on the extent of what was shared between the students.

Working together to study, sharing notes, and discussing ideas are generally considered legitimate academic collaboration. In most academic settings, students are encouraged to talk about themes, clarify concepts, and explore different ways to approach an assignment. Such discussions help deepen understanding and are not wrong in themselves.

However, the issue arises if the final essays closely resemble each other in structure, use identical examples, and follow the same sequence of arguments. Even if the wording is different, the similarity in organization and reasoning suggests a lack of independent thought. In many cases, instructors expect each student’s submission to reflect their own analysis and individual approach. When two papers appear too similar in their framework, it may be viewed as inappropriate collaboration or borderline plagiarism.

Therefore, clear boundaries must be maintained. Students may discuss general ideas and clarify concepts together, but the actual writing, structure, and selection of supporting examples should be done independently. If collaboration is permitted, students must carefully follow the guidelines provided by the instructor. When an assignment is meant to be completed individually, the final work should clearly demonstrate personal understanding and original organization.

The most responsible approach is to strictly follow the teacher’s instructions. If there is any uncertainty about how much collaboration is acceptable, students should seek clarification beforehand. Academic integrity involves learning collectively while ensuring that the submitted work genuinely represents one’s own effort and thinking.

•  A student uses two pages of their essay submitted in last semester’s course and integrates it into a new assignment without citing themselves. 

Does MLA treat this as plagiarism? What is this type of plagiarism called? What would an ethical approach look like here?

Yes, according to Modern Language Association (MLA) guidelines, this situation may still be regarded as plagiarism.

Although the student is reusing their own earlier assignment, most academic institutions and MLA standards consider it inappropriate if the material is submitted again without acknowledgment or approval.

What is this practice called?

It is known as self-plagiarism or recycling one’s work. This happens when a student submits previously completed academic material as if it were newly written for a different course or assignment, without informing the instructor.

Why is it considered problematic?

When a new task is assigned, instructors expect original work created specifically for that course. Reusing earlier material without disclosure can be misleading because it gives the impression that the student has produced fresh research and analysis, even though the content was prepared earlier.

What would be the ethical solution?

The responsible approach would include:

Seeking the instructor’s permission before reusing any previous work.

Clearly stating that certain parts were written for an earlier course.

Providing proper citation of the earlier paper if required.

Revising, updating, and expanding the old content rather than submitting it unchanged.

Academic integrity requires openness and honesty. Even if the writing belongs to the student, it should not be reused without transparency and proper acknowledgment.

Thak you.

SR: Film Screening - Humans in the Loop

Humans in the Loop: Movie Review

This blog has been written as part of the Sunday Reading Film Screening activity assigned by Dr. Dilip Barad Sir after watching Humans in the Loop. The purpose of this activity was to move beyond passive viewing and encourage students to critically engage with the film’s central ideas. It invited us to thoughtfully examine how the documentary represents artificial intelligence, invisible digital labour, and the essential role of human workers within automated systems. Through this reflective exercise, students were encouraged to analyse the realities behind technological advancement and to develop a more nuanced understanding of the social and ethical dimensions of today’s digital world.

Pre- viewing task :

1. AI Bias and Indigenous Knowledge Systems

AI bias can be understood as the systematic distortion that emerges in machine learning systems when the data used to train them reflects existing social, cultural, racial, or geographic inequalities. Rather than being neutral, AI systems inherit the assumptions and blind spots embedded in the datasets they rely on, often privileging dominant perspectives while marginalising alternative ways of knowing.

Since AI models depend on human-annotated data, the values of those who design, manage, and finance these systems inevitably shape the categories through which the world is interpreted. In Humans in the Loop, this tension becomes visible through Nehma’s experience as a data annotator. She is instructed to classify ecological entities plants, insects, animals according to industrial labels such as “pest,” “weed,” or “crop.” These rigid classifications clash with her Oraon Adivasi worldview, which understands nature relationally, contextually, and as part of an interconnected ecosystem rather than through utilitarian binaries.

The film foregrounds the relativity of such terms: what counts as a “weed” in an industrial agricultural framework may be medicinal or sacred within an Indigenous ecological context. This raises an urgent question will an extractive, consumption-driven economy ultimately dictate what counts as legitimate knowledge? Indigenous ecological knowledge (IEK), being place-based and holistic, resists the reductive logic required by machine learning architectures. In doing so, the film reveals how AI systems often reproduce capitalist and colonial epistemologies.

A powerful example appears when an AI image generator, prompted by an Adivasi child who wants to see himself riding a crocodile, instead produces an image of a white boy atop an alligator. The scene sharply illustrates how certain identities are coded as “default,” while others are erased or distorted. Ultimately, the film compels viewers to question who determines “ground truth” in AI systems and whose knowledge traditions are excluded in the process.

2. Labour and Digital Economies

Digital economies rely heavily on forms of labour that remain largely invisible. Tasks such as data annotation, content moderation, and image classification—often low-paid and feminised—constitute the hidden infrastructure behind technologies marketed as fully automated or “intelligent.”

The phrase “artificial intelligence” itself conceals the human workforce sustaining it. In India alone, tens of thousands of workers—many of them rural women—perform repetitive cognitive labour that trains and refines global AI systems. Yet this labour remains obscured: geographically distant from tech hubs, socially marginalised, and erased from the final technological product.

Humans in the Loop carefully captures the texture of this work environment—fluorescent-lit offices, slow computers, constant pressure to meet targets, and the alienation of labelling images using categories disconnected from the workers’ lived realities. By documenting these conditions, the film dismantles the myth of AI as a disembodied, neutral technology. Instead, it exposes complex supply chains of labour that echo older colonial and caste-based systems of extraction.

The film also raises ethical concerns about compensation, recognition, and intellectual ownership. Whose cognitive labour is being commodified to build billion-dollar industries? Who receives credit, and who remains invisible? By centring these questions, the documentary insists that human labour must be acknowledged as foundational rather than peripheral to narratives of technological progress.

3. Politics of Representation

Representation in Humans in the Loop operates on two intertwined levels: first, how AI systems represent or fail to represent Adivasi communities; and second, how the film itself portrays both technology and Adivasi life to broader audiences.

Public discussions surrounding the film emphasise its distinctive perspective: instead of celebrating technological advancement, it interrogates how such progress can deepen exclusion and marginalise Indigenous knowledge systems. Adivasi experience becomes not a background element but the primary analytical framework through which AI is examined.

One of the film’s most striking moments occurs when an AI image generator produces a stereotypical, Europeanised depiction in response to a request to generate a tribal woman. This scene reveals how training datasets embed colonial biases, leading AI systems either to overlook Adivasi identities altogether or to reproduce them through distorted lenses.

At the same time, debates around the film’s own representational politics are varied. Some critics commend its grounded research and authenticity, while others question whether it risks aestheticising Adivasi life for liberal, festival-oriented audiences. Such critiques highlight that representation is never neutral it is always political and structural, not merely artistic.

Even the film’s bilingual use of Hindi and Kurukh functions as a deliberate representational gesture, granting visibility to an Adivasi language rarely heard in mainstream cinema.

Taken together, the documentary encourages viewers to remain alert to a double risk: AI systems may misrepresent marginalised communities, and cinema if uncritical can replicate similar distortions. The film thus calls for ethical vigilance in both technological and cultural production.

While- watching task:

1. Narrative & Storytelling

How does the film connect Nehma’s personal life with larger algorithmic systems?

In Humans in the Loop, Nehma’s individual story is carefully woven into the broader framework of global algorithmic infrastructures. Rather than portraying data annotation as an abstract or purely technical occupation, the film embeds it within the rhythms of her domestic life, economic realities, and cultural environment. By situating digital labour inside the home, the narrative makes visible how international AI systems depend upon localised, often precarious, forms of work that remain largely unrecognised.

Key narrative moments emphasise the entanglement of labour, family, and knowledge. Scenes depicting Nehma balancing annotation tasks alongside household responsibilities foreground the gendered dimension of digital work, where the separation between professional and personal spheres dissolves. These sequences reveal that AI labour is not detached from lived experience but embedded within emotional and familial structures.

Another important narrative shift occurs when Nehma confronts the challenge of categorising ecological entities through rigid industrial labels. Her Oraon understanding of plants and animals—shaped by relational and contextual knowledge—comes into tension with algorithmic classification systems. Through this epistemic friction, the film demonstrates that annotation involves interpretation and negotiation rather than mechanical execution.

Conversations within her family and community further ground her labour in collective life, suggesting that her work cannot be reduced to individual employment alone. Visually, the film contrasts the natural environment she inhabits with the digital interfaces she navigates, underscoring the distance between lived ecological knowledge and machine-readable taxonomies. In doing so, the narrative illustrates how algorithmic systems permeate intimate spaces and reconfigure family life, cultural identity, and local epistemologies.

When Nehma “teaches” AI, what does this reveal about human–machine learning loops?

Nehma’s role in “teaching” AI challenges the notion that machine learning is autonomous or self-sufficient. The film reveals that AI systems acquire intelligence through continuous human input—absorbing judgement, contextual interpretation, and culturally shaped assumptions. In this sense, the so-called learning loop is not purely technological but deeply human-driven.

Her annotation work demonstrates that AI learning depends on subtle acts of meaning-making. Each label she assigns involves decision-making about context and relevance, showing that machines do not directly perceive reality but inherit it through mediated human perspectives. This reframes the human–machine loop as a collaborative yet unequal relationship: humans shape AI’s intelligence, yet their contributions remain invisible in the final technological product.

Moreover, what Nehma transmits to the system is influenced by her own worldview, even when constrained by industrial terminology. The learning loop thus becomes epistemological as well as technical. Certain forms of knowledge are translated into algorithmic structures, while others are compressed or excluded. The film ultimately invites viewers to reconsider AI as a socio-cultural process, raising questions about authorship, agency, and recognition. If humans are the teachers, whose knowledge becomes amplified and whose remains marginal?

2. Representation & Cultural Context

How are Adivasi culture, language, and ecological knowledge portrayed?

The film presents Adivasi culture with grounded authenticity rather than romantic spectacle. Cultural practices appear organically within everyday life through domestic routines, community interactions, and subtle markers of tradition allowing Adivasi identity to emerge as lived experience rather than as an exoticised image.

Tradition is depicted not as static but as coexisting with contemporary digital labour. Nehma’s identity as an Oraon woman shapes her relationship to work, environment, and community, illustrating continuity between ancestral knowledge and modern participation in global economies.

Language becomes a powerful signifier of identity. The contrast between local speech and the English-dominated digital interface reveals linguistic hierarchies embedded within technological systems. While her mother tongue carries cultural memory and ecological wisdom, it remains peripheral in algorithmic spaces, highlighting the unequal valuation of languages within global infrastructures.

Ecological knowledge is central to the film’s representational politics. Nehma’s understanding of nature is relational and experiential, shaped by interaction rather than abstraction. When this holistic perspective encounters reductive labels like “pest” or “weed,” the tension exposes the limits of industrial classification. Through this contrast, the film foregrounds Indigenous ecological knowledge as both resilient and epistemologically distinct.

Overall, Adivasi identity is portrayed as dynamic and adaptive. The film recognises how cultural memory and environmental knowledge persist even as communities engage with digital economies, while simultaneously pointing to the lack of adequate recognition within dominant technological narratives.

Does the film disrupt or reproduce stereotypes about tribal communities and technology?

The film largely works to dismantle prevailing stereotypes surrounding Adivasi communities and modernity. Popular media often casts tribal groups as technologically disconnected or nostalgically bound to nature. Humans in the Loop unsettles this binary by portraying Nehma as simultaneously rooted in her cultural traditions and actively participating in global AI production.

Her role as a data annotator demonstrates that Adivasi communities are not outside technological systems but are embedded within them as essential contributors. The narrative foregrounds her intellectual labour, showing her making interpretative decisions and shaping machine learning processes. This challenges the assumption that technological expertise is confined to urban or elite spaces.

At the same time, the film remains attentive to structural inequalities. By depicting precarious labour conditions and epistemic tensions between Indigenous knowledge and algorithmic frameworks, it reveals how technological systems can marginalise the very communities that sustain them. This nuanced portrayal resists simplistic celebration or victimhood narratives.

Ultimately, the documentary reframes Adivasi identity as modern, thoughtful, and technologically engaged. It shifts the focus from exclusion to recognition, suggesting that the core issue is not absence from technological futures but unequal visibility and acknowledgement within them.

3. CINEMATIC STYLE & MEANING

Mise-en-Scène & Cinematography: Humans in the Loop (2025)

Aspect Ratio — The Film’s Foundational Formal Strategy

The film adopts a 1.55:1 near-square aspect ratio, a deliberate stylistic decision that subtly echoes the proportions of a computer screen. This framing produces a contained, intimate visual field that feels almost like a storybook panel, inviting viewers into spaces and lives that mainstream cinema rarely centres. At the same time, by holding both the forest and the data centre within the same visual proportions, the film resists privileging one environment over the other. Neither nature nor technology is romanticised; both are observed with equal formal discipline.

The Forest

In the forest sequences, wide-angle compositions situate characters within the landscape rather than isolating them from it. The human figure is not positioned as dominant or oppositional to nature but embedded within its textures and rhythms.

Lighting plays a crucial role here. Natural, filtered sunlight creates warmth and softness, dispersing illumination evenly across the frame. There are no dramatic spotlights or harsh contrasts; instead, everything shares the same visual participation.

The camera frequently lowers itself to ground level most notably in the porcupine sequence placing humans and animals along the same horizontal axis. This choice visually encodes a worldview grounded in coexistence rather than hierarchy.

Compositionally, the forest resists geometry. Roots, foliage, and branches interrupt straight lines, producing irregular forms that defy order. The organic fragmentation of the frame stands in quiet opposition to the rigid grid logic of digital interfaces.

The Workspace / Data Centre

By contrast, the data centre is filmed through tighter framing mid-shots and close-ups that compress spatial depth. Walls, ceilings, and screens remain visible, giving the impression of enclosure and limiting any sense of expansiveness beyond the frame.

Lighting shifts dramatically. Fluorescent, artificial illumination flattens surfaces and removes shadow, creating a sterile environment. This flatness mirrors the binary logic of data labelling an object is categorised as one thing or another, with little room for ambiguity.

The cool blue-grey tones of the workspace sharply contrast with the earthy warmth of the forest scenes. Colour itself becomes a structural device, distinguishing pixelated environments from ecological ones.

The computer monitor functions not merely as a tool but as a dominant light source. Its glow falls onto Nehma’s face, symbolically reversing the usual relationship between subject and object. Here, the machine casts illumination onto the human body, suggesting a directional flow of authority.

Workers are often framed in rows, aligned toward their screens. This staging recalls assembly-line production, visually situating digital annotation within the history of industrial labour rather than within narratives of futuristic innovation.

Ritual and Domestic Spaces

Domestic and ritual settings are filmed with medium close-ups that feel attentive rather than restrictive. The camera lingers gently, allowing gestures and textures to unfold without urgency.

Close framing emphasises tactile materials stone, bark, woven fabric, soil foregrounding a sensory relationship to knowledge. This visual texture argues implicitly that Adivasi epistemologies are embodied and materially grounded, not easily reducible to data points.

In scenes of intergenerational exchange, eye-lines are horizontal and reciprocal. Nehma and her children meet at the same visual level, signalling equality and shared learning. This contrasts sharply with the vertical blocking in the data centre, where supervisors often stand over seated workers, reinforcing hierarchies of control.

The Central Visual Thesis

Spatial contrast becomes the film’s quiet argumentative engine. Expansive wide shots of the forest are juxtaposed with compressed interiors of the data centre, articulating a visual dialectic between openness and confinement, landscape and interface, relational knowledge and pixel-based categorisation.

The most striking articulation of this contrast appears in the parallel editing between the AI infant and Nehma’s child. Close-ups of digitised muscle-tracking data are visually echoed by intimate shots of her baby’s limbs. Through this mirroring, the cinematography invites viewers to perceive the two images as analogous. Without explicit commentary, the edit suggests that the labour invested in training the machine parallels and perhaps competes with the attention given to her own child.

Throughout, the film handles the tension between tradition and technological modernity with restraint. Instead of overt visual symbolism, it relies on subtle shifts in colour temperature, framing, and composition to carry its argument. The result is a cinematographic language that articulates conflict and coexistence through atmosphere rather than exposition, allowing form itself to become the site of critique.

How do sound design and editing rhythms contribute to the contrast between analog life and digital labour?

The Sound Team

Sound Design: Kalhan Raina

Score: Saransh "Khwabgah" Sharma

Editing: Swaroop Reghu & Aranya Sahay

Division of Creative Labour

The film’s formal coherence emerges from a clearly structured collaboration. Raina shapes the diegetic universe the tangible world the characters inhabit. Sharma constructs the non-diegetic emotional layer through music, guiding how the audience feels without intruding upon the narrative space. The editors, Swaroop Reghu and Sahay, regulate duration and pacing, determining the tempo at which scenes unfold. Together, they control space, emotion, and time what exists, what resonates, and how long each moment is allowed to linger.

Sound Design: Organic vs. Mechanical Worlds

At the most fundamental level, the film builds a sonic opposition between living environments and technological ones. This contrast is not decorative; it structures the film’s political argument.

Forest and Domestic Soundscapes

In the forest and home environments, sound is layered and non-hierarchical. Bird calls, rustling grass, flowing water, insects, children’s chatter, and communal singing coexist without a dominant centre. The soundscape feels fluid and unpredictable. Rather than isolating a single sonic element, the mix allows multiple textures to overlap, producing a polyphonic field that mirrors a relational ecological worldview.

Significantly, these sounds retain their rough edges. Background hums, environmental interference, and acoustic imperfections are preserved rather than polished away. This refusal to sanitise the track reinforces a sense of embodied presence. Living sound is treated as textured and irreducible not as a clean, optimised signal. The auditory texture itself becomes an argument for the complexity of organic life.

The Data Centre’s Contracted Sound

In contrast, the acoustic field inside the data centre narrows dramatically. The dominant sounds are repetitive and mechanical: keyboard tapping, mouse clicks, fluorescent buzzing, system boot tones, and the faint lag of aging computers. Unlike the forest’s layered unpredictability, these sounds are regular, rhythmic, and uniform.

The mouse click, in particular, takes on symbolic weight. Each click finalises a decision—pest or not pest, crop or weed, valid or invalid. It is a minimal, almost dry sound, yet it carries the force of classification. In its brevity and finality, the click performs the violence of binary reduction. What appears insignificant acoustically becomes politically charged: a single sound enacts the transformation of complex life into data.

The Score: A Deliberate Sonic Bridge

Sharma’s musical approach combines organic instrumentation—guitar and piano—with synthetic textures and ambient electronics. Drawing from ambient, post-classical, and downtempo influences, the score unfolds gently in layered compositions that feel tactile and introspective. Rather than dominating scenes, the music provides a subtle tonal undercurrent.

Importantly, the score does not segregate sonic worlds. Acoustic instruments are not reserved solely for forest scenes, nor are electronic textures confined to technological spaces. Instead, both registers coexist within the same compositions. This blending mirrors Nehma’s lived reality: she inhabits both ecological and digital worlds simultaneously. The music refuses a simplistic binary, just as her life refuses neat categorisation.

In moments such as the opening porcupine sequence, the ambient score adds a dreamlike resonance. The music elevates the visual event without overstating it, transforming a small encounter into something quietly mythic. Yet even here, restraint governs the composition. The score does not swell or dictate emotion. Its understatement leaves interpretive space open an ethical gesture in a film concerned with who has the authority to define meaning.

The Kurukh Music Question: Balancing Authenticity and Legibility

One of the film’s most thoughtful sonic decisions concerns the integration of Kurukh music. Oraon musical traditions often resist fixed metre and stable key structures. Rhythms shift, tonalities change mid-performance, and the flow does not conform to predictable cinematic timing.

The challenge, then, was how to honour this musical authenticity without alienating audiences accustomed to regularised rhythmic patterns. The inclusion of synthesizers, violin, and other bridging instruments creates a hybrid space. These elements provide enough familiarity to anchor listeners while retaining the distinctiveness of Kurukh musical logic.

This negotiation is itself political. The score becomes an audible site of translation between knowledge systems between irregular Indigenous musical structures and the metrical expectations of mainstream cinematic spectatorship. Crucially, the director acknowledges this compromise transparently. The act of naming the negotiation becomes part of the film’s ethical stance.

Editing: Time as Political Form

Editing functions as another site of ideological expression. With Sahay directly involved in the edit, pacing becomes inseparable from thematic intent.

Slowness in Ecological Sequences

Forest and domestic scenes unfold in longer takes with minimal cuts. The edit allows moments to accumulate organically. Encounters—whether between Nehma and a porcupine or between mother and child—are not hurried. This slower tempo encodes an alternative experience of time: attentive, non-industrial, and unmeasured by productivity.

Acceleration in Labour Sequences

Within the data centre, the rhythm tightens. Cuts become more frequent, mirroring the pace of annotation work. Images flash in succession, echoing the rapid click-rate required of workers. The viewer is subtly trained into the same cognitive tempo as Nehma. In doing so, the editing implicates the audience in the rhythm of classification it critiques. The film makes us feel the compression of attention.

The Parallel Edit: AI Infant and Guntu

The most striking editorial gesture occurs in the intercutting between Nehma annotating infant muscle data and close-ups of her own son Guntu’s limbs at home. The temporal rhythm remains consistent across both spaces. The edit neither dramatizes nor accelerates the juxtaposition.

By maintaining equal pacing, the film argues that these two acts occupy the same temporal unit. The labour given to the machine and the care given to the child share the same measure of time and attention. The cut itself makes the argument no explanatory dialogue or emphatic score is required. The linkage is formal, not rhetorical.

Silence as Ethical Space

Restraint ultimately defines the film’s sonic and editorial philosophy. Silence recurs at crucial moments not as emptiness, but as charged suspension. After Nehma refuses to classify a caterpillar as a pest and faces reprimand, the scene is followed by stillness rather than dramatic music.

This silence is deliberate. It withholds emotional instruction and creates a gap. In that gap, the audience must confront the implications of what has occurred. Who determines meaning? Who speaks next? By refusing to fill the space, the film shifts responsibility to the viewer.

Through sound, score, editing, and silence, the film transforms formal choices into political ones. Its argument is not delivered through overt declaration but through rhythm, texture, and restraint allowing aesthetics themselves to carry critique.

4. ETHICAL & POLITICAL QUESTIONS

What ethical dilemmas are depicted when training AI with culturally specific data?

1. Who Decides the Category? — The Epistemic Conflict

A pivotal moment occurs when Nehma refuses to classify a creature as a “pest,” drawing on her community’s ecological understanding that the organism does not harm crops. Her refusal becomes grounds for reprimand. Through this incident, the film exposes the rigidity of algorithmic systems that depend on fixed, universal categories while dismissing localised, experience-based knowledge.

The deeper ethical issue concerns authority: who determines what counts as valid knowledge? The AI’s taxonomies are structured for an industrial, export-driven agricultural economy, largely shaped by clients located in the Global North. In contrast, Nehma’s insights emerge from generational observation rooted in a specific landscape. Ironically, her contextual knowledge arguably more accurate within her environment is treated as error because it does not conform to pre-set classifications. The system does not evaluate truth; it enforces compliance. Nuance, complexity, and relational understanding are sacrificed to fit machine-readable boxes.

2. Extraction Without Return — The Logic of Data Colonialism

The film also points to a second dilemma: the extraction of value from marginalised communities without meaningful compensation. Datasets that include images of Indigenous people, their languages, and their ecological knowledge are built through the labour and cultural contributions of communities like Nehma’s. Yet the benefits generated by these enriched datasets flow elsewhere.

As AI systems become more “representative” through the inclusion of Adivasi faces and knowledge, the question arises: who profits from this representation? Global technology companies depend on the data labour of rural Indian workers, yet those workers neither own the systems they help build nor share in their financial rewards. Nehma’s cultural knowledge is absorbed into a commercial infrastructure over which she has no control.

This dynamic exemplifies data colonialism: the appropriation of epistemic resources images, language, lived knowledge from communities positioned at the margins of global power structures. Extraction occurs not through land or minerals, but through data. Consent, credit, and compensation remain absent.

3. Inherited Bias — The Cycle of Reproduction

The film further explores how AI systems do not produce neutral outcomes; they inherit the assumptions embedded within their training. As Nehma begins to recognise that the machine learns through her labour, she confronts an unsettling realisation: the technology she helps build may replicate the very forms of exclusion she experiences.

Machine learning systems absorb the biases embedded in their classification frameworks. These frameworks are designed elsewhere, yet enacted through the labour of women like Nehma. In this sense, she occupies a paradoxical position. She is subject to systemic discrimination, yet through constrained annotation practices, she may also participate unwillingly in reproducing new forms of bias.

The ethical tension lies here: can someone simultaneously be marginalised by a system and implicated in sustaining it? The film suggests that structural inequality makes such contradictions inevitable.

4. Representation as Vulnerability — The Image Dilemma

When Nehma attempts to correct the AI generator’s distorted representations by uploading her own image and those of her community, the film introduces another ethical complication. AI companies increasingly seek culturally diverse datasets to improve accuracy and expand markets. Yet the communities supplying these datasets rarely receive recognition or agency in return.

To counter misrepresentation, Nehma must submit herself to the same technological apparatus that previously erased her identity. Visibility comes at a cost. In order to be accurately “seen,” she must surrender her likeness to a system structured around commercial value rather than human dignity.

This creates the film’s most acute ethical bind: inclusion becomes another mode of extraction. Representation does not automatically equal empowerment; it may simply render communities more legible for further appropriation.

5. Diffused Responsibility — The Accountability Gap

The film also dissects the hierarchy through which algorithmic decisions are produced. Dataset guidelines, client briefs, and managerial oversight embed dominant assumptions long before Nehma begins labelling images. What appears as technological neutrality is in fact structured by layered decisions about whose faces matter, which categories dominate, and what stories are excluded.

The chain of authority stretches across geographies: international tech clients define objectives; local managers enforce compliance; data workers execute instructions. Responsibility is distributed across this chain in such a way that accountability dissipates. No single actor appears solely responsible for the system’s consequences.

The outsourcing architecture itself functions as insulation. By fragmenting labour and decision-making, it prevents ethical responsibility from settling in one place. The film raises the pressing question of collective accountability: who answers for the algorithms being built in humanity’s name?

Core Ethical Proposition

Across these interconnected dilemmas runs a unifying argument: the ethics of artificial intelligence cannot be separated from existing structures of caste, gender, class, and geography. Training data is never neutral. It carries the imprint of those who conceptualise, fund, and supervise its creation.

When the burden of producing that data falls on communities already positioned at the margins of global power, the imbalance intensifies. The film insists that AI systems do not merely reflect technical design choices; they encode social hierarchies. Every dataset carries an ethical residue. And when that residue originates from unequal worlds, the moral debt accumulates at every level of the algorithmic chain.

How does the film’s human-in-the-loop metaphor operate beyond the technical term—politically, socially, and culturally?

The Technical Definition

In machine learning terminology, human-in-the-loop (HITL) describes a system in which human oversight improves algorithmic performance. A person reviews outputs, corrects errors, and refines the model through repeated feedback cycles. Within this framework, the human figure appears empowered positioned as evaluator, guide, and guarantor of accuracy. The term suggests balance, agency, and collaborative benefit.

Humans in the Loop begins from this technical premise but gradually unsettles the assumptions it carries. The film asks: what if the “loop” is neither balanced nor beneficial to the human participant?

The Political Loop: A Circuit of Extraction

The title gestures toward a closed feedback system between humans and machines each shaping the other in an ongoing cycle. Yet the film reveals that this loop is asymmetrical. Economic and epistemic value moves in one direction, while constraint and discipline move in the other.

Cognitive labour and cultural knowledge flow upward from Jharkhand to global technology hubs where they are processed into profitable AI products. What returns is not empowerment but standardisation: pre-determined categories, productivity metrics, and hierarchical oversight. This structure mirrors older forms of colonial extraction. Instead of raw materials like minerals or crops, the resource being mined is human interpretation and cultural insight.

The loop is also self-perpetuating. Biased systems generate biased outputs. Those outputs further marginalise the very communities whose labour trained the system. Nehma corrects the algorithm, but the economic architecture simultaneously reshapes her informing her which knowledge counts, which distinctions are permissible, which realities are recognised. She remains inside the loop, but the loop does not circulate in her favour.

The Social Loop: Caste, Gender, and Class as Recursion

The film situates Nehma’s work within broader social hierarchies she did not choose. Her presence in the data lab is layered atop existing structures of gender, caste, and class.

As a woman, her labour whether domestic or digital is treated as endlessly available and undervalued. The system depends on her attentiveness yet offers limited recognition. The metaphor extends subtly: a world driven by domination and efficiency sidelines relational, nurturing modes of knowledge.

Caste introduces another layer of irony. A job centred on rigid classification is outsourced to communities whose lived realities exceed simple binaries. Workers are trained to categorise the world in machine-friendly terms, even as their own identities defy reductive labels. To teach machines how to simulate humanity, they must temporarily suspend their own pluralities.

Class dynamics ripple forward through the next generation. Dhaanu’s attraction toward a more urban, upper-caste identity signals assimilation as a recursive social pattern. Hierarchies reproduce themselves not through a single act of exclusion but through repetition across generations.

In this framing, the “loop” signifies more than a technical cycle. It becomes a social recursion an exclusionary system that regenerates itself through institutions, families, and now algorithms.

The Cultural Loop: Translation as Erasure

The film’s most original expansion of the metaphor lies in its epistemological dimension. During training sessions, AI is described as childlike it must be taught how to see. This metaphor transforms into a site of contestation. If the machine is a child, who determines its education? What worldview is encoded in its lessons?

Nehma enters the loop with a deeply relational ecological knowledge system. Yet the transmission of that knowledge occurs through categories she did not author. A caterpillar must be classified as either harmful or harmless; a plant as weed or crop. Her understanding is filtered through binary logic before entering the dataset.

The loop therefore does not fully absorb her knowledge. It converts it into simplified data, strips away its contextual richness, and reintroduces it into the world as “objective” information. The machine learns from her labour but fails to recognise her existence. She trains the system to see, yet remains unseen within it.

The cultural loop reaches its sharpest point here: she is indispensable to the process, yet invisible as a subject within it.

Mutual Training: An Unequal Exchange

The film’s most incisive insight is that training operates in both directions. While Nehma teaches the algorithm, she is simultaneously conditioned by its demands. To feed the system, she must think within its constraints suppressing intuitive, relational reasoning in favour of industrial binaries.

The violence of this loop is not only extractive but substitutive. The AI economy does not simply take her knowledge; it replaces it with a compressed version and asks her to internalise that reduction. The loop reshapes cognition itself. In teaching the machine to process the world in rigid categories, she risks being reshaped by those categories in return.

A Meta-Loop: The Film’s Own Dilemma

Some critics argue that the film may replicate the very structure it critiques. While addressing marginalisation, it risks positioning Adivasi experience as material for global festival audiences. In highlighting data bias, it may inadvertently frame Indigenous life through an external lens. In condemning invisibility, it may not fully redistribute visibility or agency to the community depicted.

Rather than undermining the film’s central metaphor, this critique intensifies it. The “human-in-the-loop” problem extends beyond data centres into cultural production itself. Representation, like data annotation, can become another circuit of extraction if not accompanied by structural reciprocity.

Thus, the loop expands: from algorithmic systems to social hierarchies to cinematic representation. The film suggests that the challenge is not merely to insert humans into technological systems, but to question how those systems are structured and who ultimately benefits from the loop’s circulation.

POST-VIEWING REFLECTIVE ESSAY TASKS

TASK 1 — AI, BIAS, & EPISTEMIC REPRESENTATION

Critical Reflection: Humans in the Loop (2025)

Technology, Knowledge, and the Politics of Classification

Introduction

Contemporary discourse frequently frames artificial intelligence as autonomous, self-optimising, and fundamentally impartial. Humans in the Loop (2025), directed by Aranya Sahay, dismantles this assumption from its very first moments. Set among Adivasi communities in Jharkhand, the film follows Nehma, an Oraon woman employed as a data labeller in an AI training facility. Through her experience, the film reveals a core paradox: systems marketed as objective are constructed from human labour that is categorised, extracted, and subordinated often from those positioned furthest from technological power.

This essay contends that Humans in the Loop portrays algorithmic bias not as a glitch awaiting correction, but as an inevitable consequence of epistemic ordering. The issue is not faulty code but the politics of classification: who defines categories, whose worldview is embedded in datasets, and whose existence is rendered legible to the machine. Drawing on Louis Althusser, Jean-Louis Baudry, and Stuart Hall, the film can be understood as both a story about digital labour and a formal critique of knowledge hierarchies in the age of AI.

Algorithmic Bias as Structured Perspective

The film’s most revealing conflict unfolds around an unassuming creature: a caterpillar. Assigned to categorise it as a “pest” within an agricultural AI system, Nehma resists. From her Oraon ecological understanding shaped by lived, generational knowledge the caterpillar feeds on decomposing plant matter and contributes to regeneration. Within her epistemology, it is not destructive but necessary.

Her supervisor Alka, bound by productivity metrics and directives from an American tech client, overrides this assessment. Nehma is instructed not to “use her brain.”

The moment crystallises what feminist science theorist Donna Haraway calls situated knowledge: all knowledge arises from particular historical and material positions. The AI’s categories pest/non-pest, weed/crop reflect the priorities of industrial monoculture agriculture. They are designed for optimisation and yield, not ecological reciprocity.

The conflict is not about miscommunication. It is about incompatibility. To integrate Nehma’s ecological logic would require rethinking the economic assumptions that underpin the AI system. The suppression of her judgment is therefore not incidental but systemic. The machine functions precisely as intended: it stabilises the worldview of those who commissioned it.

Bias, the film suggests, is not an error within the system. It is the architecture of the system.

Apparatus, Visibility, and Ideological Framing

In his essay “Ideological Effects of the Basic Cinematographic Apparatus” (1974), Jean-Louis Baudry argued that cinema’s technical structure naturalises a centred, unified spectator. The apparatus produces ideology not through content alone but through the organisation of vision itself.

Humans in the Loop extends this logic to AI systems. The question becomes: what kind of subject does the algorithm presume, and what forms of life remain outside its perceptual field?

This question is dramatised when Nehma and members of her community prompt an AI image generator to depict a “tribal woman.” The output is a pale, Europeanised figure. The system reproduces the statistical dominance of Western faces within its training data and presents this output as normative. The failure is not merely representational; it is structural. The dataset has encoded absence as universality.

Here, Louis Althusser’s concept of ideological state apparatuses becomes instructive. Ideology does not simply misrepresent the world; it produces subjects who recognise themselves within its categories. The data centre in the film functions in precisely this way. Nehma is trained not only to classify images but to internalise the classificatory logic itself. She must learn to suppress relational thinking in favour of binary decisions.

The “human in the loop” is thus not the master of the machine but a subject gradually reshaped by it.

Representation and Epistemic Hierarchy

Stuart Hall’s theory of representation clarifies the deeper stakes. Meaning, Hall argues, is constructed through representational systems governed by power. The film juxtaposes two such systems: Adivasi ecological knowledge, which is contextual and interdependent, and algorithmic categorisation, which seeks universality and scale.

The hierarchy at work is not simply preferential but ontological. Nehma’s interpretation of the caterpillar is not acknowledged as alternative expertise; it is dismissed as error. Her understanding is excluded from the domain of valid knowledge altogether.

This dynamic aligns with philosopher Miranda Fricker’s concept of epistemic injustice: harm enacted upon individuals specifically in their capacity as knowers. Nehma’s labour is exploited, but more profoundly, her knowledge is delegitimised.

The film maps the flow of authority that enables this injustice. An American tech client defines classification standards from afar. An Indian manager enforces these standards locally. Nehma performs the cognitive labour at the base of the chain. The closer one is to the data, the further one is from defining what counts as accurate.

“Neutrality” dissolves when traced through this hierarchy. Categories are not discovered they are imposed.

Form as Argument

The film’s critique is embedded not only in dialogue but in form. Its 1.55:1 near-square aspect ratio echoing the proportions of a computer monitor situates the audience within a frame that recalls the interface of classification itself. We observe Nehma through a screen-shaped space, mirroring her own act of viewing and labelling. The spectator occupies the same visual constraint as the machine.

The contrast between environments reinforces this argument. Forest sequences are composed through wide angles, organic asymmetry, and warm, diffused light. Human and non-human life share horizontal planes. The visual field is textured, layered, and non-hierarchical.

In the data centre, by contrast, tight framing compresses space. Fluorescent lighting flattens depth. The blue glow of monitors illuminates Nehma’s face, reversing the usual hierarchy of light — the machine casts illumination onto the human. Authority is literally directional.

The film’s most incisive formal gesture arrives in its parallel editing sequence. Nehma annotates infant muscle-movement data to train an AI walking model while her own child, Guntu, begins to walk at home. The cuts align the rhythms of these two acts. Close-ups of digital motion correspond to close-ups of human limbs.

The equivalence is deliberate. The same attentiveness required to nurture a child is redirected toward the machine. Cognitive care becomes extractable labour.

The argument unfolds without explicit commentary: artificial intelligence is not built from abstraction alone. It is assembled from human time, perception, and relational attention. What appears autonomous is sustained by lives that remain unacknowledged within its outputs.

Conclusion:

Humans in the Loop advances a clear and pressing claim: what we call “algorithmic bias” cannot be repaired by better code alone. It is not a software malfunction but the visible trace of much older hierarchies hierarchies about whose knowledge is legitimate, whose categories structure reality, and whose judgments carry authority. Unless those prior arrangements of power are addressed, no technical refinement will fundamentally alter the system’s logic. Through Nehma’s journey, the film reveals that AI taxonomies are not universal descriptors of the world but culturally produced frameworks shaped by dominant economic priorities. The same communities whose labour and insight are mined to train these systems are excluded from determining what counts as accurate within them.

Read through apparatus theory, the AI system resembles cinema’s own technical machinery: a structure that presents its perspective as transparent and objective while quietly organising vision according to ideological assumptions. Stuart Hall’s account of representation clarifies that meaning never arises neutrally; it is constructed within regimes of power. The AI’s inability to generate recognisably Adivasi faces is therefore not a minor flaw but a predictable result of whose images saturate the dataset and whose visual norms define the “default.” Louis Althusser’s notion of ideological apparatus further sharpens this analysis. The data centre does more than extract labour; it shapes workers into subjects who internalise its classificatory logic, aligning their cognition with the needs of the system.

Ultimately, the film shifts the conversation away from optimisation and toward accountability. It poses a question that lies beyond computation: what kind of social order are we reproducing through the systems we design? The answer cannot emerge from the algorithm itself. It belongs to the political and economic structures that commissioned it and to the viewers, thinkers, and citizens willing to confront what the machine is structured not to perceive.

Thank you.