Diversity, Equality, and Inclusion in Our Professions: A Thin and Leaky Pipeline
Bibliographic record
Abstract
The world is moving forward and our leaders appear to be set to make Diversity, Equality and Inclusion (DEI) a household name. The DEI abbreviation has become a frequent topic appearing on many agendas, it appears in publications and lectures, studies are devoted to it, symposiums are being organized, committees are sprouting up, and politicians say they are committed to it. There is no doubt, DEI has become a topic for the working and political worlds. Governments, universities, and many organisations are setting quotas to achieve DEI. Yet, data shows that in the Science, Technology, Engineering and Mathematics (STEM) professions, DEI is lagging, thus begging the questions: “Where is DEI standing in STEM?” and “Why should we, as STEM researchers and as a not for profit scientific society be devoted to DEI?”
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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.025 | 0.070 |
| Scholarly communication | 0.035 | 0.067 |
| Open science | 0.002 | 0.039 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".