User-centered policy design: challenges and opportunities of its application for social policy in Canada
Bibliographic record
Abstract
Access to policy development is typically limited and exclusive, seldom including stakeholder groups representing marginalized individuals. The voices of end-users, however, are essential to creating effective, accurate, and targeted policies and services that truly consider the perspectives of those who are the most impacted. User-centered design (UCD) is considered by scholars and practitioners to be a potential solution to this issue. Central to this is the idea that individuals directly impacted by government policies are actively involved in identifying policy solutions. Practical application of UCD, however, yielded mixed results with the recent literature noting that marginalized populations continue to be excluded from the opportunity to participate or their perspective are excluded as being not representative of general population. This paper examines UCD potential to address existing service delivery challenges within the child welfare system in Ontario, Canada. It offers important insights on benefits and limitations on UCD’s potential for policy development and design involving marginalized populations. We conclude that recruitment of representatives from marginalized groups on its own is not sufficient to develop fully implementable policy solution(s). Nevertheless, voices of marginalized individuals are crucial for advancing our understanding why they are disproportionately impacted by the “color-blind” public policies.
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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 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.029 | 0.024 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".