Jennifer Wallner, Learning to School: Federalism and Public Schooling in Canada
Bibliographic record
Abstract
Unlike many federations, Canada's education system is highly decentralized, with the constitution granting exclusive jurisdiction over education to the provinces, authority which has been fiercely defended.In 1976, a report of the Organization for Economic Co-operation and Development found wide inconsistencies and a lack of national goals and standards across Canada's provincial education systems.Yet by the 2010s, there was remarkable consistency across these systems in their structures, funding mechanisms, class sizes, and high outcomes on international tests.In Learning to School, political scientist Jennifer Wallner sets out to determine how the provinces managed to establish this overarching system of education.She reconsiders the processes that are traditionally believed to underpin the formulation of policy frameworks in federations.Central to her thesis is a challenge to the assumption that a consistent approach requires the intervention of a central authority with the power to compel action by sub-state governments.The book aims to demonstrate that a coherent and consistent provincial policy framework in Canada need not be the result of coercion or competition.Rather, it can be the outcome of cooperation and mutual learning, via the sharing of ideas through collaborative institutions and policy networks.Wallner aims to determine what factors enable the diffusion of certain policies.She attempts to advance the "second movement" in institutionalist analysis, drawing on three different strains of institutionalist theory (methodological, sociological and historical).Moreover, she claims to be adding an "ideational turn" to the study of federalism by showing that ideas matter and can spread across policy networks, influencing the actors working within them.She states that her approach has "crafted a new analytical architecture for understanding the alternative dynamics of policy
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".