Performing Diversity: Navigating Tensions, Identity Threats, and Self-Instrumentalization in Applicant Diversity Statements
Bibliographic record
Abstract
Applicant diversity statements require job candidates to describe their personal contributions and commitment to Diversity, Equity, and Inclusion (DEI), yet there is limited research on how applicants experience and navigate this emerging practice. We employ a sequential mixed-methods design with open-ended surveys and in-depth interviews with academic job candidates to explore how applicants manage self-presentation when crafting diversity statements. Our findings reveal two key self-presentation tensions: a Self-Disclosure Tension—whether and how much to reveal personal identities and experiences in an instrumental setting—and a Framing Tension—how to package those identities and experiences to balance institutional expectations with authentic self-expression. These tensions, triggered by ambiguity about the task’s purpose and content, produce two identity-related threats: Trivialization Threat—the fear of one’s identity being undermined and reduced to token gestures—and Exploitation Threat—the sense that one’s identity is being used for institutional gain. By centering the applicant perspective, our study extends research on instrumental DEI framing, identity management, and organizational performativity by highlighting the self-instrumentalization required by applicants navigating ambiguous and evaluative DEI practices. Practically, our findings underscore the unintended consequences of performative DEI practices that shift the burden of proof onto individuals without institutional accountability.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".