Beyond Backlash: Advancing Dominant-Group Employees’ Learning, Allyship, and Growth through Social Identity Threat
Bibliographic record
Abstract
Current scholarship about dominant-group employees’ experiences of social identity threat highlights how threat can lead to backlash, undermining these employees’ support for marginalized-group employees at work. We alternatively suggest that social identity threat can also inspire dominant-group employees to learn how to better support marginalized-group employees. Leveraging intergroup threat theory and applying transformational learning theory, our theoretical model describes how, and the conditions under which, social identity threat can trigger a process of learning whereby dominant-group employees update their interpretations of dominant and marginalized social identity groups, and relations between the groups. We also note implications of learning for employees’ allyship behaviors and growth. Recognizing that learning occurs via interactions with colleagues, we introduce dialogue across perspectives as a way for dominant-group employees to obtain feedback and update their interpretations. Moreover, we elucidate individual and organizational factors that facilitate both openness to learning in response to social identity threat, and the likelihood of dialogue across perspectives occurring in organizations. Ultimately, while prior theory has described the perils of social identity threat, our theory speaks to the silver lining of threat for dominant-group employees’ learning, allyship, and growth in organizations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".