Implementing EDI across a large formal research network: Contributing to equitable and sustainable water solutions for a changing climate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.234 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.392 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.090 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it