Implementing EDI across a large formal research network: Contributing to equitable and sustainable water solutions for a changing climate
Bibliographic record
Abstract
Increasingly, large transdisciplinary research networks focusing on pressing global problems are being encouraged through research investment strategies. One of the challenges is understanding how inequities in these networks shape scientific practice in environmental research. While granting agencies in Canada frequently require some equity metrics, these tend to focus on four specific equity-deserving groups. It is argued that this does not go far enough. Global Water Futures, a large cold region water research network, introduced an EDI strategy and implementation framework in 2021. The process began with a critical review of the literature, consultations within the network, and a desire to translate cutting-edge EDI knowledge and practices into a form that could be operationalized. Embedded in the approach is an intersectional lens that considers how power structures differentially impact people based on race, gender, 2SLGBTQIA+ identity, disability, and more. The implementation framework gives structure to EDI to further the program’s transdisciplinary research commitments and as a transformative set of actions to support new ways of working that challenge power dynamics in water research. Towards effective EDI, the strategy considers institutional relationships, research impact, and knowledge mobilization. Other natural resource sectors and environmental organizations working to integrate the UN Sustainable Development Goals laterally may be able to learn from this process to frame and implement EDI towards more inclusive water research. Given the climate emergency, working together towards sustainable and equitable solutions is critical, disrupting hegemonic research norms and engaging diverse voices and knowledge systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchScience and technology studies Domain: Incentives · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.239 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.008 | 0.065 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".