Transforming public policy with engaged scholarship: better together
Bibliographic record
Abstract
Many people remain invisible in all stages of policymaking processes and are re/harmed by policy decisions made in their absence, even where public engagement has occurred. This lack of meaningful engagement affects those experiencing homelessness, migrant workers, northern and Indigenous women, and others with whom we have collaborated. This article demonstrates the transformative potential of recognising these ‘invisible’ actors as legitimate and effective actors in the policy process. In this article, we present a series of Canadian research vignettes, emerging from our empirical research programmes, that illuminate the possibilities for the principles of engaged scholarship to advance transformative, community-driven policymaking. Along with other critical policy scholars, we are concerned about how power circulates and is distributed unequally through public policy. Our focus in on how commitments manifested through engaged scholarship can disrupt these power distributions. Through our vignettes, we demonstrate how principles of engaged scholarship can shape public engagement, the understanding of policy problems, the creation of evidence, and the development of meaningful policy solutions. This article makes an important contribution on how to improve the processes of public engagement in policy development.
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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 | 0.089 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.042 | 0.094 |
| Scholarly communication | 0.058 | 0.060 |
| Open science | 0.006 | 0.057 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".