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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".