Exploring Media Misrepresentation: A Critical Ethnography of the Portrayal of South Asian Women in Television
Bibliographic record
Abstract
<p>The media is considered one of the most powerful tools in society as it creates, shapes and perpetuates ideologies about marginalized communities (Glavinic, 2010). The portrayal of cultural groups in the media can shift audiences’ perception of a particular culture, but may contain inaccuracies or simplistic depictions which do not fully encompass their experiences. In this study, I sought to disrupt the hegemonic representation of South Asian women within the media and acknowledge the diverse lived experiences and narratives within this community. I conducted a secondary data analysis on the portrayal of South Asian women in selected popular television shows, analyzing the following characters: Kelly Kapoor from <em>The Office</em>, Mindy Lahiri from <em>The Mindy Project</em>, and the Vishwakumar family from <em>Never Have I Ever</em>. Employing a critical ethnography approach framed by critical race feminist theory and Foucault’s conceptualization of power to advance knowledge on how mainstream television conceptualization of South Asian women. The study revealed that the media continues to oppress and marginalize South Asian women through the perpetuation of cultural and racial stereotypes. This study challenges dominant narratives, shedding light on representation that better reflects, celebrates, and appreciates women of the South Asian community.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".