Bibliographic record
Abstract
Abstract This book advances a new concept of the “Asian diaspora” that creates links between Asian American, Asian Canadian, and Asian Australian identities. Drawing from comparable studies of the black diaspora, it traces the histories of colonialism, immigration, and exclusion shared by these three populations. The work of Asian poets in each of these three countries offers a rich terrain for understanding how Asian identities emerge at the intersection of national and transnational flows, with the poets’ thematic and formal choices reflecting the varied pressures of social and cultural histories, as well as the influence of Asian writers in other national locations. Diasporic Poetics argues that racialized and nationally bounded “Asian” identities often emerge from transnational political solidarities, from Third World struggles against colonialism to the global influence of the American civil rights movement. Indeed, I show that Asian writers disclaim national belonging as often as they claim it, placing Asian diasporic writers at a critical distance from the national spaces within which they write. As the first full-length study to compare Asian American, Asian Canadian, and Asian Australian writers, the book offers the historical and cultural contexts necessary to understand the distinctive development of Asian writing in each country, while also offering close analysis of the work of writers such as Janice Mirikitani, Fred Wah, Ouyang Yu, Myung Mi Kim, and Cathy Park Hong.
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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 | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".