Bibliographic record
Abstract
This article deploys text mining and quantitative analysis to survey the breadth of the Asian American literary corpus and the scholarship framing it. We have built a database covering all scholarship in the MLA bibliography, Amerasia, and the Journal of Asian American Studies that studies a literary work under the rubric of Asian American. For the works and authors cited, we collected a wealth of metadata from publisher and genre to gender, ethnicity, and more. Asian Americanists have long debated the definition of Asian American literature, but we have not traced the choices of scholarly attention that have accreted over decades and hundreds of publications to shape a canon. The results here reveal the systemic effects and inequalities generated by those choices. They confirm a long-suspected bias toward contemporary literature. They reveal troubling ethnic inequalities. The literatures of Asian American ethnic groups beyond the six most studied groups receive minimal attention. Korean American literature has leaped to second most studied, resulting in a reconfigured East Asian American hegemony: Chinese, Korean, and Japanese. This was enabled by a troubling decline in studies of Filipinx American literature, once central to the field. Much Filipinx American literature is today studied outside the Asian American framework entirely. Meanwhile, the conflation of Chinese American literature with Asian American literature has intensified. The field’s rhetoric of diversification has masked persistent inequalities in our critical practices. More encouragingly, the corpus has surpassed gender equity, placing women writers at the center of the field. The work of building the Asian American corpus we would want is far from over. Data-driven methods can be powerful allies in the self-scrutiny necessary to this work.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".