Asian Exceptions, Racial Illegibility, Relational Spaces: A Dialogue on Asian Canadian, Asian American, and US Southern Studies
Bibliographic record
Abstract
The essays that follow argue for the adoption of more expansive and multifaceted approaches to understanding Asian diasporic relations within Canada and the US and between the “souths” of both nations. Lai resists hegemonic formulations that force comparisons between state powers and also assessments of the perceived “belatedness” of Asian Canadian Studies; to expand our understanding of Asian Canadian expression and relations, she argues for greater attention to non-academic cultural productions as well as interstitial histories. Lee expands definitions of racial in-betweenness to look at geopolitical formulations including nationalism and settler colonialism; he argues that the discomforting affects stimulated by these historical movements should also be included in analysis of transpacific migrations. Kim, examining Season One of Serial (2014), a documentary podcast series by This American Life, highlights the instability of racial categories, unevenness of racial identification and dis-identification among individuals, and cultural effects of a racial analytics that frequently “locate[s] Asians outside of the North American everyday.” Bow, noting a bias within Asian American Studies to favor post-1965 decolonization and migration narratives over a much earlier hemispheric history of indentured Asian laborers, draws attention to the functions of US immigration laws and nativist sentiments in authorizing state management racial categories “Asian” at different times and for different purposes. Cha demonstrates the pedagogical opportunities contemporary works such as Monique Truong’s novel Bitter in the Mouth (2010) open up through their rejection of traditional formulations of regional and ethnic literature and provision of a more “syncretic vision” of the US South. Ho, in the final essay, highlights the many productive discrepancies brought to light by the question “Where are you from?”, which for her summons the many global, local, hemispheric, regional, migratory, and diasporic interstitial crossings of her family.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.075 | 0.047 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".