Realizing the Promise of Disaggregated Data and Analytics for Social Justice Through Community Engagement and Intersectoral Research Partnerships
Bibliographic record
Abstract
In Canada, community and policy leaders have issued urgent calls to collect, analyze, and mobilize disaggregated data to inform equity-oriented initiatives aimed at addressing systemic racism and gender inequity, as well as other social inequities. This essay presents critical reflections from a national Roundtable discussion regarding how meaningful community engagement within academia–community–government research collaborations offers the opportunity to harness disaggregated data and advanced analytics to centre and address the priorities of equity-deserving and sovereignty-seeking groups. Participants emphasized four key priorities: (1) Building equitable and engaged partnerships that centre community-driven priorities and address structural barriers to community engagement; (2) Co-creating ethical data governance policies and infrastructure to support community data ownership and access; (3) Stimulating innovation and pursuing community involvement to create contextualized, advanced analyses and effective visualizations of disaggregated data; and (4) Building the capacity of all partners to effectively contribute to partnership goals. Capacity building was viewed as a bridge across a diversity of lived and professional expertise, enabling intersectoral research teams to collaborate in culturally safe and respectful ways. Beyond identifying key structural barriers impeding the promise of disaggregated data, we present practical opportunities for innovation in community-engaged scholarship to address social justice challenges in Canada.
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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 | 0.082 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.030 | 0.043 |
| Scholarly communication | 0.043 | 0.021 |
| Open science | 0.005 | 0.053 |
| Research integrity | 0.004 | 0.010 |
| 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".