Equity, diversity and inclusion in management mathematics: from policy to practice, with urgency
Bibliographic record
Abstract
Abstract Accepted by: Prof. Aris Syntetos In May 2025 a panel discussion took place at the 5th Institute of Mathematics and its Applications (IMA) and Operational Research (OR) Society Conference on Mathematics of Operational Research. The panel catalysed a vital conversation on Equity, Diversity and Inclusion within management mathematics. While policies proliferate, lived experiences reveal persistent gaps: ethnic-minority researchers face lower funding odds, minoritized students encounter higher dropout rates and many initiatives risk performativity. While this editorial focuses primarily on UK-based challenges and responses, it also draws on parallel experiences from international mathematical communities—in the USA, Canada, Australia and Europe—to situate national efforts within a broader global discourse. These comparisons highlight shared structural barriers and offer transferable models of policy and practice.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".