Imagining South Asian America: Media Activism, Immigration, and Race in the Digital Age
Bibliographic record
Abstract
This research project analyzes the expression of South Asian American identity by South Asian American podcasters, to study efforts to foster internal and external solidarities within and by this community. South Asian American as an identity is comprised of those living in United States or Canada with heritage from Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. People of this community have taken to digital media in the form of podcasts to share voices of the South Asian diaspora in America to a widespread audience of both people who are part of the community and people who want to listen to the community. These activists have utilized digital media to establish a space for their identity and to rectify the lack of representation of South Asian American voices in digital media. Each student chose two podcasts to listen to, paying special attention to themes, host and guest invocations of identity, tone and conversational flow, and overall sound and quality of production for each episode. We shared our observations in weekly meetings: quotes highlighting powerful ideologies, short clips of insightful moments, and general notes on the topics discussed in the episode, before writing individual executive summaries detailing these research findings. By listening to the first-hand experiences of South Asian Americans through podcasts, we observed that the identity created by these podcasters via digital media traversed subethnic, regional, racial, and religious boundaries that often exist in the physical world and mainstream media representations. Some of these boundaries include higher education, choosing an unconventional career path, and having an unconventional significant other. This led us to conclude that the South Asian American identity established by activists via podcasts is diverse, multidimensional, not confined to roles imposed by a racialized society, and is created with the intent to create solidarity within the 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.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".