Flourishing together: Leveraging social‐personality psychology in community building for scholars of color
Bibliographic record
Abstract
Abstract Many faculty of color (FOC) lack the necessary social and professional support structures to facilitate their successful advancement on the tenure track. We present Flourish, a network for pre‐tenure social and personality psychologists of color, as a case study to illustrate how an affinity group model can aid in supporting FOC. We demonstrate that using social support to diffuse social identity threats can increase belonging and academic fit and, as a result, promote the success of early career FOC. As a field, psychological science has the potential to transcend the boundaries of academic institutions by leveraging our theories, methods, and applied expertise to build infrastructure to better support FOC. Attracting and retaining a diverse faculty workforce addresses racial inequities in higher education, diversifies research output, and makes our science more representative and reproducible. Thus, investing in affinity groups is essential for developing accessible social support systems and increasing role models for future scholars of color.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".