Breaking Borders, Bridging Fields: Unveiling the Transculturality of Anti‐Asian Racism in a Global Context
Bibliographic record
Abstract
Despite the recent surge in scholarly attention to anti‐Asian racism, what is largely missing in this growing body of literature is a bridge connecting studies on this subject to the broader field of race and ethnicity studies. In this special issue, we propose to use the concept of transculturality, which is defined as the process of cultural interaction, interpenetration, and hybridization that transcends the traditional borders of individual cultures, to establish this connection. In this introductory article, we first critically review the concepts of culture, interculturality, and multiculturality in the studies of race and ethnicity. Upon this review, we explain how transculturality advances the knowledge of racial and ethnic identity, ideas, and practices. This introduction concludes with an overview of each contribution, setting the stage for a comprehensive exploration of this complex and multifaceted issue. Collectively, this special issue aims to not only provide theoretical and empirical insights into the transculturality of anti‐Asian racism but also build a bridge between the studies of the Asian diaspora and the general research on race and ethnicity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".