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>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".