Policy advocacy to enable administrative data linking: building a civil society coalition
Bibliographic record
Abstract
IntroductionAdministrative data linking holds tremendous promise for improving understanding of social problems, enhancing service delivery, and revolutionizing impact evaluation in Canada. Embracing this opportunity at scale requires navigation of significant technical and policy challenges. The greatest challenge, though, may be a lack of political will. Objectives and ApproachThe nonprofit sector is uniquely positioned to advocate for a strong political commitment to linked administrative data. As a sector, it could directly benefit from that data for impact evaluation and for advocacy. It is also closest to the people who are most likely to be negatively impacted by the resulting surveillance and stigmatization. We are building a network of social service organizations, foundations, and advocacy groups to explore the possibility of creating a shared policy agenda. We’ve developed a coalition model that engages these unequally resourced stakeholders on equal footing - with the goal of enabling fully-informed and equitable participation. ResultsThis coalition is working to develop a set of conditions for increased administrative data linking that reflect the shared interests of funders, service providers, advocacy groups, and beneficiary communities. We are also researching the legislative and policy changes required to enable that desired outcome. In developing this agenda, and bringing it to government, we hope to provide the social license (and public pressure) required to create an enabling policy environment for increased data linking in Canada. Beyond developing a shared agenda, this initiative also aims to deliver long-term outcomes involving increased data policy literacy among Canadian nonprofits. This coalition represents collaborative infrastructure to enable ongoing, coordinated input from the nonprofit sector on key questions of data governance and policy. Conclusion/ImplicationsThis equity-focused, multi-stakeholder coalition approach to digital policy development represents a significant innovation in public engagement. We’re excited to share our process and key learnings with conference participants with hopes of receiving expert feedback while inviting key allies to engage in this emerging initiative.
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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 | 0.125 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.049 | 0.024 |
| Scholarly communication | 0.037 | 0.019 |
| Open science | 0.010 | 0.054 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".