Advancing Equity in Geosciences: Insights and Actions from the Canadian EDI Landscape
Bibliographic record
Abstract
The geosciences are at a pivotal moment as institutions, organizations, and individuals confront long-standing inequities to create a more inclusive and representative future. As a geoscientist actively engaged in equity, diversity, and inclusion (EDI) initiatives, I have witnessed both the barriers and breakthroughs shaping this transformation. Notably, the geosciences have some of the poorest metrics for diversity, equity, and inclusion (DEI) in STEM disciplines. Guided by the principle, “What gets measured, gets done,” my work has focused on quantifying EDI impacts to drive meaningful progress.Drawing on my role as an executive member of the Canadian Geophysical Union’s EDI Committee, I will present key findings from a comprehensive EDI report on representation statistics from Canadian Geophysical Union conferences since 2018. As a director on the board of Women Geoscientists in Canada, a prominent organization supporting women in technical roles, I will highlight the challenges and successes in addressing gender imbalance and improving diversity within the mining industry.Lastly as a federal research scientist working on critical mineral exploration and green energy transitions, I will explore how EDI efforts can advance community engagement, inclusive excellence, interdisciplinary collaboration, ethical fieldwork, and environmental justice. By sharing these experiences across government, industry, and academia, this presentation will offer actionable strategies to address barriers and inspire collaboration for a more equitable future in Canadian geosciences.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.064 | 0.028 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".