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
<JATS1:p>As a distinct area of literary study, Asian American literature now enjoys a level of critical recognition that was unimaginable when academic interest in the field began modestly some 25 years ago. Part of this recognition stems from the increasing contributions of Asian American novelists, whose works continue to capture growing levels of popular attention. By the early 1970s, anthologies of creative writing by Asian Americans began to appear, and there are now almost two dozen of them. Since then, numerous Asian American writers, such as Amy Tan, Michael Ondaatje, and Bharati Mukherjee, have gained considerable critical and commercial success.</JATS1:p> <JATS1:p>The publication of this reference work reflects the new academic status of Asian American literature. Included are alphabetically arranged entries for 70 Asian American novelists. Since the historical and current experiences of Asians in Canada and the United States are substantially similar, the volume covers authors from both countries. While the majority of the writers profiled in the volume have East Asian backgrounds, some have South Asian or West Asian origins. Each entry is written by an expert contributor and provides a short biography, a discussion of major works and themes, a summary of the novelist's critical reception, and separate bibliographies of primary and secondary sources. The volume concludes with a selected, general bibliography.</JATS1:p>
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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