Bibliographic record
Abstract
The thesis develops a model of the "catalytic" state characterized by a capacity for effective industrial policy formation and effective implementation of selected policies resulting in successful long run processes of economic transformation. Over the past two decades analysts have increasingly turned to state capacity as a central variable for the explanation of economic performance. The model of the catalytic state emerges from debates on industrial policy and the developmental state. The focus is on economic policy priorities, state organizational arrangements and institutional links with non-state economic actors as central features of state capacity. The research applied the catalytic state model to Ontario's agricultural sector by asking whether and in what forms Ontario's agricultural policy could be considered catalytic over the period between 1791 and 2001. The research demonstrates that the state in the nineteenth century played a limited catalytic role in the agricultural sector but did provide some important infrastructure and the beginnings of programs encouraging human capital formation. Since the late nineteenth century the state has played a strong catalytic role, pursuing a consistent productivist agenda employing a variety of policy instruments including support to agricultural research, education, extension, market management, investment and income distribution programs. The thesis concludes that the state in Ontario has been catalytic with respect to long run transformation of the agricultural sector and has retained this capacity even over the past twenty years as the policy paradigm has shifted from Keynesianism to neoliberalism. This evidence is interpreted as supporting the argument that state capacity is an important contributor to processes of successful long run economic transformation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".