The anti-DEI agenda: navigating the impact of Trump’s second term on diversity, equity and inclusion
Bibliographic record
Abstract
Purpose This article aims to critically analyse and critique the impact of President Donald Trump’s second term on Diversity, Equity and Inclusion (DEI) initiatives in the United States of America and beyond. It aims to document the policy changes enacted under Trump, explore the resulting consequences for various sectors (government, higher education and private sectors) and assess the broader implications for social justice and global conversations around equity. The article also serves as a call to action, urging continued advocacy and resistance against the rollback of DEI progress. Design/methodology/approach This viewpoint article offers a range of perspectives on the impact of the anti-DEI agenda from several DEI scholars who edit the journal. This critical approach draws from multiple sources of information to enrich and provide a more nuanced understanding on the impact of Trump’s policies on DEI. Findings President Trump’s anti-DEI agenda had a profound and far-reaching impact on DEI efforts, creating a climate of fear, exacerbating existing inequalities, intensifying political polarization and having ramifications beyond the borders of the United States of America. The paper also highlights the indirect and unintended consequences, such as the chilling effect on private sector DEI initiatives and the rise of “shadow” DEI programs. Research limitations/implications The article draws from secondary sources (news articles, policy documents and existing research). The authors’ scholarships on DEI and the Trump administration’s negative stance towards DEI might influence their perspectives and conclusions. Practical implications The authors close with a strong call to action, encouraging continued advocacy and resistance to the erosion of DEI progress. This pragmatic approach moves beyond simple criticism and offers concrete steps for future action. They suggest that the current anti-DEI sentiment represents a temporary setback in the long-term progress towards equity and inclusion. Social implications President Trump’s anti-DEI policies have global implications, influencing policy and practice in other countries and impacting international conversations around fairness and equity. Originality/value The article provides a valuable record of the significant shift in DEI policy and practice under the second Trump administration. This account will be a crucial resource for future researchers studying this period and its impact. This critical analysis also provides valuable insights for both scholars and activists working to advance DEI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.071 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.222 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".