Bibliographic record
Abstract
Asian American Literature: An Encyclopedia for Students is an invaluable resource for students curious to know more about Asian North American writers, texts, and the issues and drives that motivate their writing. This volume collects, in one place, a breadth of information about Asian American literary and cultural history as well as the authors and texts that best define it. A dozen contextual essays introduce fundamental elements or subcategories of Asian American literature, expanding on social and literary concerns or tensions that are familiar and relevant. Essays include the origins and development of the term “Asian American”; overviews of Asian American and Asian Canadian social and literary histories; essays on Asian American identity, gender issues, and sexuality; and discussions of Asian American rhetoric and children’s literature. More than 120 alphabetical entries round out the volume and cover important Asian North American authors. Historical information is presented in clear and engaging ways, and author entries emphasize biographical or textual details that are significant to contemporary young adults. Special attention has been given to pioneering authors from the late 19th century through the early 1970s and to influential or well-known contemporary authors, especially those likely to be studied in high school or university classrooms.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.119 | 0.040 |
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".