Imagining New Racial Politics: Identity Work and Coalition Building in South Asian American Podcasts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This project examines the coalitions and expressions of identity forged through podcasts by South Asian American hosts and producers. South Asian American is a coalitional identity label that encompasses people who live in the United States and Canada and trace their heritage to the South Asian subcontinent, consisting today of Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Fighting against internal divisions and outward misrepresentation, the young, digitally-savvy advocates of coalitional South Asian America reflect critically on their identities and histories to build radical futures. We mapped and catalogued a network of podcasters who are actively negotiating the politics of this emergent South Asian American identity, specifically looking at podcasts which are hosted and/or run by someone in the South Asian diaspora in North America, or has an intended South Asian American audience. Additionally, our work required careful analysis of the podcasts’ sound design, language of community or affinity, host and guests brought together, discussion topics or themes, building of the relationship between hosts and audiences, and overall listening experience. In particular, we carefully analyzed the ways that the intimate soundwork and confidential conversations so central to this genre of podcasting are productive tools for forging internal and external solidarities based on shared experiences of racialization. Podcasting, talking together as a community in a sincere and personal manner, is a crucial way South Asian Americans process their positionality and foster a sense of community through lived experiences. Podcasting allows for deep listening that feels lively, sociable, and co-present, and for the honoring of other’s personal experiences. This creates a meaningful platform for listeners and hosts to work through their understanding of themselves as part of a larger, emerging social justice-oriented community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it