Coalition advocacy action and research for policy development
Bibliographic record
Abstract
Coalitions of individual or institutional actors play a critical role in the process leading up to changes in policies. However the lack of proper conceptualization of their actions, and more broadly of the general policy change process, impedes the capacity of public health authorities to learn from the experience in the field. In this chapter we describe two theoretical frameworks. Sabatier and Jenkins-Smith’s Advocacy Coalition Framework allows for a better understanding of the conditions leading to significant change in policies while Lemieux’ theorization of coalition building and structuring provides the key to understand how coalitions succeed or fail in pooling the resources of their members and in seizing up the opportunities to influence the policy change process. We conclude the chapter by applying the frameworks to tobacco control advocacy in Quebec; highlighting the benefits of an approach to policy research and advocacy grounded in sound theories.
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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 | 0.040 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".