Imagining South Asian America: Reclaiming the South Asian American Experience Through Podcasts
Bibliographic record
Abstract
This project examines how South Asian Americans use podcasts to hold meaningful conversations about identity, history, and community. South Asian Americans live in the United States or Canada, and have ethnic roots in the Indian subcontinent (e.g., Pakistan, Nepal, etc.). Often stereotyped as apolitical technology enthusiasts, South Asian American podcasters use their platform to push against the narratives that are frequently placed on them by their elders and community outsiders, discussing topics that are often ignored in diasporic South Asian communities. We each closely analyzed one podcast and cataloged and categorized over 10, unpacking the podcasts’ language, themes, formats, and sound design. We discussed our findings with each other on a weekly basis, sharing impactful quotes and clips to gain additional insight on our observations. Through this work, we observed the complexities that South Asian American podcasters face in initiating dialogues about topics such as identity, representation in popular American media, mental health, and South Asian history. Although they cover narratives that are unique to South Asian Americans or the South Asian diaspora, these shows often begin with the intention of being palatable to all ethnic demographics. Notably, as time progressed, many shows we studied altered their content to speak mainly to South Asian American listeners, frequently because that is who seemed to be listening and responding. At the same time, other aspects of the podcasters’ identities, such as class status, age, family immigration history, and gender posed limits for seeking solidarity with their audience, despite their intentions to unify. Overall, we found that an increasing number of South Asian Americans are using podcasting as a platform for sharing their unique cultural experiences. Their abilities to bridge gaps with their audiences is nuanced, yet powerful. Through podcasts, these creatives are reshaping what it means to be a South Asian American.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".