Beyond Backlash: Reducing Resistance and Generating Support in Response to Diversity Initiatives Through Opening Identity Tactics
Bibliographic record
Abstract
Although researchers are recognizing that dominant social identity threat towards diversity initiatives can result in backlash, researchers have paid limited attention to how dominant social identity threat can be in service of support for diversity. This dissertation considers how identity exploration after dominant social identity threat can facilitate responses that move members of dominant social identity groups towards diversity support rather than diversity resistance. First, I I bridge theory on identity threat and uncertainty regulation to birth a comprehensive model of how identity threat can lead employees belonging to dominant social identity groups to engage in closing and opening identity tactics. Closing identity tactics re-affirm one’s hierarchy-maintaining knowledge about membership to dominant social identity groups, while opening identity tactics transform one’s understanding of membership to dominant social identity groups, so that the focus becomes one that is less about maintaining hierarchy and more about challenging inequalities. Then, I document in Study 1 that participants show greater engagement in opening identity tactics after reading about an organization’s diversity initiatives when they complete an opening identity tactics intervention, which in turn, came to explain why participants were more likely to report valuing of diversity and organizational identification toward the organization. Finally, I further document in Study 2 that White employees of organizations with existing diversity initiatives showed a stronger relationship between their engagement in opening identity tactics and valuing of diversity after completing a six-week opening identity tactics intervention (versus a control condition). Overall, my dissertation challenges a widely held assumption that dominant social identity threat is only a roadblock to the advancement of diversity and inclusion in organizations. Rather, I show how dominant social identity threat can also trigger positive identity changes that translate into support for diversity. In doing so, my research has implications for understanding the benefits and costs of diversity initiatives and dominant social identity threat.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".