Beyond Backlash: Advancing Dominant-Group Employees’ Learning, Allyship, and Growth through Social Identity Threat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it