Dismantling DEI with Narratives to Justify a Radical Change
Bibliographic record
Abstract
The landscape of Diversity, Equity, and Inclusion (DEI) in the U.S. is currently experiencing unprecedented volatility. Adopting a historical narrative perspective, our study examines the narrative strategies employed by U.S. public universities in response to anti-DEI legislation. Through thematic analysis of 63 official statements from these institutions, we identified three narrative types that universities use to align their responses with their historical stances on diversity: linking changes to a recent pro-diversity past, reverting to a more distant monocultural history, and leveraging historical narratives to validate longstanding programs against recent DEI initiatives. These findings illuminate the pivotal role of historical narratives in shaping DEI responses and highlight how history can be strategically manipulated to justify the institution’s responses to change. Additionally, this study reveals the varied narrative strategies institutions employ to redefine the concept of diversity in response to evolving legal frameworks. This analysis contributes to our understanding of how institutions use historical narratives to navigate and influence their strategic responses to legislative changes in the DEI arena.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".