Navigating Intra-Racial Dynamics: Psychological Needs and Self-Group Distancing Among East Asians
Bibliographic record
Abstract
Research on biases and stereotypes against East Asian (EA) workers has typically focused on inter-racial encounters. Accordingly, we have a limited understanding of how these stereotypes affect intra-racial dynamics among EA professionals. Drawing from self-determination and social identity threat theories, this experimental vignette study examines how psychological needs shape how EAs manage their identities and navigate intra-racial encounters in the workplace. EA professionals in the U.S. and Canada will be randomly assigned to one of three experimental conditions (relatedness, autonomy, competency) or a neutral task, followed by measures of self-group distancing and knowledge management behaviors. Activating relatedness is expected to reduce self-group distancing and in turn knowledge hiding compared to activating autonomy or competence. Data collection will begin February 2025. Our study contributes to a more inclusive research agenda by examining how biases and stereotypes against EAs affect intra-racial group dynamics, encouraging further exploration to an understudied group in organizational research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".