Generational Differences in Responses to Diversity Rationales
Bibliographic record
Abstract
Because employee buy-in has been identified as a critical component of change with respect to diversity, organizations frequently attempt to make diversity-related events seem more appealing by offering reasons for why one should attend them. For example, organizations justify these diversity-related events as learning opportunities to attain practical skills in a diverse world, opportunities to promote social justice, or judgment-free spaces to discuss sensitive topics. It is important to empirically test whether providing these justifications is effective, because the signals they send can unintentionally hinder the very goals that they aim to achieve. It is particularly critical to examine the effects of these diversity justifications in an organizational setting with multiple stakeholders, because the ways that organizations discuss diversity can elicit varying responses across individuals from different backgrounds. In this paper, we examine whether these managerial practices are actually effective in engaging the individuals that they target in a multi-stakeholder field context. In two pre-registered field experiments at a large academic institution (N = 47,807), we find that providing a justification has a positive effect on younger generation’s registration and attendance rates, whereas it has a negative effect on the older generation’s engagement in these events, after controlling for other relevant factors such as gender, race, and staff/student status. In a follow-up lab experiment, we find evidence that organizations’ attempts to influence people via diversity justifications affect older generation adversely, but not younger generation, due to a difference in the sense of obligation that they feel toward the organization. Based on research on psychological reactance (Brehm, 1966), we theorize that diversity justifications trigger disengagement among those who feel more obligated to attend (e.g., older generation) due to a perceived threat to their autonomy.
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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 | 0.028 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".