The complex chemistry of diversity and inclusion: a 30-year synthesis
Bibliographic record
Abstract
Dr. Margaret-Ann Armour’s career as a research chemist, educator, and advocate spanned more than 40 years. Much of her work took place within a disciplinary culture ignorant of the scholarship supporting organizational change towards inclusive excellence. Her contributions are extensively covered in other articles in this special issue, and her achievements are all the more remarkable given that her colleague, Dr. Gordon Freeman, held gender-biased attitudes that he shared in a peer reviewed article in a national science journal. Three decades later, another Canadian chemist, Dr. Tomáš Hudlický, published a peer reviewed essay in an international chemistry journal that included his views on the negative impacts of diversity initiatives on organic synthesis research. Both articles were retracted, but clearly a faulty and pervasively biased peer review system enabled the distribution of prejudiced opinions that were neither informed by demonstrated expertise, nor supported by data. These two events are reflective of challenges that Dr. Armour faced in her efforts to diversify chemical sciences. We need to build on her critical work to increasing awareness about inclusive excellence in chemistry, as well as educating scientists on what constitutes an informed opinion. Here, we use Freeman and Hudlický incidents as case studies to indicate how pervasive bias can be superficially perceived as scientific scholarship. Furthermore, we use analogies of analytical processes to illustrate how talent gets systemically excluded. Finally, we provide recommendations to chemistry community members for improving outcomes in terms of synthesis of new knowledge, ideas, and solutions, toward leveraging all the available human talent and creating an environment that is both excellent and inclusive.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".