The Future of Equity, Diversity, and Inclusion (EDI) in Education
Bibliographic record
Abstract
Equity, Diversity, and Inclusion, also known as EDI, is one of several acronyms that refer to policies and practices of organizations, such as K to 12 school boards and post-secondary institutions, to promote inclusive and equitable environments. While EDI has become a ubiquitous discourse in educational administration, its impact extends into classroom pedagogies, hiring practices, curriculum development, and institutional culture. In this article, we critically examine the extent to which EDI committees and initiatives fulfil their promise of fostering inclusive environments to support equity-denied groups. We address the following research questions: (1) How have EDI frameworks evolved in education? (2) What gaps exist between EDI policies and their implementation for transformative impact? (3) What alternative approaches can create sustainable and impactful change? Overall, the article casts a critical eye on EDI initiatives, exploring opportunities, challenges, and potential futurities considering the polarizing discourse of political correctness and anti-wokeness.
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 | 0.031 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.111 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".