Governance Structures and Political Processes in a Public System: Lessons from Quebec
Bibliographic record
Abstract
Debates about the form and nature of changes in the management of public systems have for some time now been articulated under the theme of governance. All definitions of governance are related to the problems of securing convergence among a diversity of actors and organizations, of redistributing power in an organizational or social field characterized by a high level of heterogeneity and of gaining sufficient legitimacy to act in the name of the collectivity. This paper concentrates on the dynamics involved in the emergence of new governing capabilities in public systems. More precisely we study the implementation of regionalization policies in the health care field (in this case of Canada) conceived as one major attempt to renew the governance structures in a large public system. Our study draws on the comparative analysis of empirical regulation in three health regions as contrasted with three ideal type models of governance in regionalized systems. The first model is inspired by an economic approach to organizational behaviour and focuses on mandate‐giving, execution and control. The second model is based on a political interpretation of behaviours and focuses on negotiation processes between actors. Finally, the third model is based on the theory of deliberation as well as the institutional school of policy analysis and focuses on direct public participation in public affairs. Evidence for this article is primarily derived from interviews with key informants and documentary analysis. Our analysis shows that none of the three models is sufficient in itself to grant regional structures the authority and legitimacy they need to create added value in terms of regulation that could ensure their survival. Regional Boards are thus forced by environmental constraints to conceive and implement original mixes of these models. The two predominant logics in regional action are inspired by the first two models although the last one also has an influence. Overall, our study also suggests that the implementation of new sources of governing authorities in a public system is rather fragile because of dependence on existing institutions. Clearly, the attempt to modify the dynamics of governance in a given system must be conceived as a political exercise and not just as a technical problem consisting of the rational adjustment of policy instruments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".