Critical Issues Facing Asian Americans and Pacific Islanders in Organizations and Society
Bibliographic record
Abstract
The discussion of Asian Americans and Pacific Islanders (AAPIs) in the context of the West is uniquely complex. AAPIs are often held up as “model minorities,” resulting in exclusion from many equity conversations. The lack of attention focusing on the experiences of AAPI communities in organizations and society suggests a need for us to remedy this. In this special issue, we curated a collection of eight papers that tackle a broad range of issues that advance conversations of AAPI communities and diasporas. We contend that it may be particularly beneficial to take a critical perspective (using Asian Critical Theory or AsianCrit) to bring to light and challenge systemic issues faced by AAPI communities in Western workplaces and societies. We also call for a post-model minority narrative, which has the potential to mitigate the adverse impacts that the notion of a model minority has on both intragroup and intergroup relations and well-being.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.029 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| 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".