Diversity Messages That Invite Allies to Diversity Efforts
Bibliographic record
Abstract
Organizational diversity messages are essential for promoting inclusion and diversity. Yet, these messages can backfire because they are often met with noncompliance or resistance from dominant group members. The current research tests whether adding an ally invitation to diversity messages can mitigate this backlash by showing allies the role they can play in organizational diversity efforts. Seven studies (n = 6,404) support this theory. Study 1 found that dominant group member employees were more concerned than minority group member employees about whether they belong in and can contribute to diversity efforts in their current workplace. Five experiments then found that tailoring diversity messages to address these concerns (i.e., ally invitation diversity messaging) reduced dominant group members’ backlash compared to traditional diversity messages (Studies 2a, 2b, 3, 4, Supplemental Study 1) and no diversity messages (Study 4). Furthermore, ally invitation messaging can increase dominant group members’ anticipated involvement in diversity efforts (Study 5). Crucially, minorities responded as positively to the ally invitation message as traditional pro-diversity messages (Study 3). Together, these results suggest that diversity messages that highlight both diversity and the role of allies can effectively garner support from dominant group members and minorities and create more diverse workplaces.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".