MIGDAL GOES CANADIAN: Deconstructing the ‘Executive’ in the Study of Canadian Federalism
Bibliographic record
Abstract
Over the 20th century the field of comparative politics was subject to a debate about the proper way of theorizing the state. Society-centric scholars initially put the state in the background, while later state-centric authors brought the state back in, making it the focal point of their analysis. Dissatisfied with both, Joel S Migdal published State in Society (2001), which advocated for a rethinking of the study of the state. Migdal argued that the state must be considered as a fragmented actor among many others in society. This theory of fragmentation of the state would seem to be naturally applicable to the study of federalism. Yet this has not been the case. This paper argues that Migdal’s approach would be a useful addition to the study of federalism and intergovernmental relations, using Canada as a test case. A brief review of some key literature first places Migdal’s approach in terms of the broader debate between ‘societalists’ and ‘statists’. Migdal’s approach is then applied to a particular facet of the literature on Canadian federalism: executive federalism. The paper concludes that although federalism in Canada has been studied extensively, Migdal’s notion of ‘state in society’ would provide us a useful way to further our understanding of federalism and intergovernmental relations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.022 | 0.027 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".