Whole‐of‐society approach for public health policymaking: a case study of polycentric governance from Quebec, Canada
Bibliographic record
Abstract
In adopting a whole-of-society (WoS) approach that engages multiple stakeholders in public health policies across contexts, the authors propose that effective governance presents a challenge. The purpose of this paper is to highlight a case for how polycentric governance underlying the WoS approach is already functioning, while outlining an agenda to enable adaptive learning for improving such governance processes. Drawing upon a case study from Quebec, Canada, we employ empirically developed concepts from extensive, decades-long work of the 2009 Nobel laureate Elinor Ostrom in the governance of policy in nonhealth domains to analyze early efforts at polycentric governance in policies around overnutrition, highlighting interactions between international, domestic, state and nonstate actors and processes. Using information from primary and secondary sources, we analyze the emergence of the broader policy context of Quebec's public health system in the 20th century. We present a microsituational analysis of the WoS approach for Quebec's 21st century policies on healthy lifestyles, emphasizing the role of governance at the community level. We argue for rethinking prescriptive policy analysis of the 20th century, proposing an agenda for diagnostic policy analysis, which explicates the multiple sets of actors and interacting variables shaping polycentric governance for operationalizing the WoS approach to policymaking in specific contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".