Policy Rogue or Policy Entrepreneur? The Forms and Impacts of “Joined-Up Governance” for Child Health
Bibliographic record
Abstract
Joined-up governance (JUG) approaches have gained attention as mechanisms for tackling wicked policy problems, particularly in intersectoral areas such as child health, where multiple ministries that deliver health and social services must collaborate if they are to be effective. Growing attention to the need to invest in early childhood to improve health and developmental trajectories, including through developmental screening, illustrate the challenges of JUG for child health. Using a comparative case study design comprised of the qualitative analysis of documents and key informant interviews, this work sought to explain how and why visible differences in policy choices have been made across two Canadian jurisdictions (Ontario and Manitoba). Specifically, we sought to understand two dimensions of governance (structure and process) alongside an illustrative example-the case of developmental screening, including how insiders viewed the impacts of governance arrangements in this instance. The two jurisdictions shared a commitment to evidence-based policy making and a similar vision of JUG for child health. Despite this, we found divergence in both governance arrangements and outcomes for developmental screening. In Manitoba, collaboration was prioritized, interests were aligned in a structured decision-making process, evidence and evaluation capacity were inherent to agenda setting, and implementation was considered up front. In Ontario, interests were not aligned and instead decision making operated in an opaque and siloed manner, with little consideration of implementation issues. In these contexts, Ontario pursued developmental screening, whereas Manitoba did not. While both jurisdictions aimed at JUG, only Manitoba developed a coordinated JUG system, whereas Ontario operated as a non-system. As a result, Manitoba's governance system had the capacity to stop 'rogue' action, prioritizing investments in accordance with authorized evidence. In contrast, in the absence of a formal system in Ontario, policy 'entrepreneurs' were able to seize a window of opportunity to invest in child health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".