“Shining Armour”: what Margaret-Ann Armour taught us about equity, diversity, and inclusion and mentorship in the natural sciences
Bibliographic record
Abstract
In addition to her contributions to the field of chemistry, Dr. Margaret-Ann Armour was the foremother of equity, diversity, and inclusion in the natural sciences in Canada and was an exemplary mentor to many women in science, technology, engineering, and mathematics. Dr. Armour emphasized that, to make progress in natural sciences and engineering fields, we also need to make advancements in workplace EDI. Dr. Armour was among the first to recognize the need to fix gender biased systems and not women. Analyses of the 2017–2018 Faculty Workplace Climate Survey, administered to approximately 700 natural sciences and engineering professors from 13 Canadian universities, supports Dr. Armour’s position. We present a synthesis of the key findings from the survey, which speak to some of the gendered challenges that women faculty members in Canada still face; and discuss the implications of these findings in light of women’s continued lack of access to mentors, with an emphasis on gender bias in mentorship within academic chemistry.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.033 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".