Multi-level governance framework and its applicability to education policy research - the Canadian perspective
Bibliographic record
Abstract
Education policies are increasingly characterized as complex and dynamic, involving a multitude of actors and policy networks. As a result, there is a growing demand in education for research approaches that can help make sense of this complexity. This paper examines the applicability of multi-level governance (MLG) framework as a tool of education research from Canada’s decentralized federalist perspective. By conducting a comprehensive literature review of 50 peer-reviewed journal articles, we determine the applicability of MLG framework, the conditions necessary for its use, and its overall relevance to education policy, which is increasingly characterized by the involvement of a variety of stakeholder groups across government levels and policy sectors. The key findings are presented following Bowe et al.’s (1992) policy cycle framework. We conclude that MLG approach is a strong tool for education research to analyze policy making in federal decentralized educational systems, as it allows a more nuanced perspective for understanding the multilayered policy dynamics often unfolding in the context of federalism.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".