Bibliographic record
Abstract
Abstract Hemispheric approaches to Asian American literature disrupt, supplement, and interrogate the cultural nationalist focus of early Asian American studies, transpacific and transnational approaches to Asian American studies that came to prominence in the 1990s, and the overall dominance of the United States in Asian American studies. These approaches have largely been championed by scholars working in Canada or on Canadian material, by feminist and queer scholars, and by those working on interethnic or interracial approaches between “Asian American” and black/African American, Latinx, and/or Native/Indigenous communities. The term “hemispheric” was preceded by Asian North American, which has been employed from fairly early in the maturing of Asian American literary and cultural criticism. Key also is the scholarly history of hemispheric approaches to Asian North American literature and culture (and to a lesser extent Asian Caribbean and Latin American literature and culture), the cross-border relations between artists and activists of Asian descent in North America, and the U.S. cultural imperialism inherent in this approach as well as its potential to diversify and open up the field of Asian American literary and cultural studies. The hemispheric approach also uncovers some of the limitations of the “transnational” and “diaspora” approaches that currently dominate Asian American studies and emphasize an east-west, transpacific spatiality.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".