Diversity, equity and inclusion work: a difference that makes a difference … ?
Bibliographic record
Abstract
Purpose I examine if current diversity, equity and inclusion (DEI) initiatives can actually accomplish what they aim and claim to do. I argue that perforce they cannot, as they remain instruments of capitalist corporations and other similar structures. Design/methodology/approach I draw on a variety of literature, from poetry to theories and to empirical findings. Findings DEI work so far does not live up to its hyped-up claims. It is time for scholars and practitioners to question the DEI industrial complex and its influence on organizational dynamics. It is not clear that justice can ever be achieved in a capitalist neoliberal economy. Research limitations/implications The paper is not an empirical paper. Practical implications DEI work needs to be re-conceived so that it addresses power imbalances, rather serving as a tool to keep organizations comfortable in seeming to change. Social implications DEI practitioners will need to draw deeply on their courage so that they do not reinforce the existing systems of capitalist oppression through their well-intentioned work. Originality/value The paper argues that DEI work can accomplish little without a radical reconceptualization of its nature as a genuine tool for change, rather than simply window dressing.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".