A personal account of contributions to equity, diversity and inclusion within the Canadian chemistry community
Bibliographic record
Abstract
On June 14, 2022, at the Canadian Chemistry Conference and Exhibition in Calgary, Alberta, I delivered the award lecture for the inaugural Canadian Society of Chemistry (CSC) Gilead Award for Excellence in Equity, Diversity, and Inclusion. The lecture was scheduled in a special symposium entitled, “Affecting Institutional Equity, Diversity, and Inclusion (EDI) Mindsets”, organized by Professor Stephanie MacQuarrie, Cape Breton University, Dr. Nimrat Obhi, Beyond Benign, Professor Dipesh Prema, Thompson Rivers University, Dr. Janelle Sauvageau, National Research Council, Dr. Bryony McAllister, Transport Canada, Professor Nola Etkin, University of Prince Edward Island, and Professor Jean-Denys Hamel, University of Lethbridge. Fig. 1 shows photos from the award reception with representatives from Gilead Sciences, the Chemical Institute of Canada (CIC), and the CSC Working towards Inclusion, Diversity, and Equity (WIDE) working group, then committee. This paper is an excerpt of the award lecture with a focus on my 10+ years of contributions to EDI within the Canadian Chemistry Community. Efforts to address equitable access to education and research in chemistry are ongoing in many countries around the world. Here, Professor Hind A. Al-Abadleh provides a personal account of her contributions to equity, diversity, and inclusion in the Canadian chemistry community.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.145 | 0.033 |
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".