Bibliographic record
Abstract
The Ontario Places to Grow Plan, finalized in 2006, marks the boldest attempt to address urban sprawl in Canada, and arguably North America. Among its many components, the Plan establishes a permanent greenbelt covering roughly 1.8 million acres (728,000 hectares) of environmentally sensitive land. This article seeks to explain the political calculations and conditions that led to the Ontario policy. I argue that the Plan was partially devised to garner support in key suburban ridings (electoral districts) across the Greater Toronto Area in the 2003 provincial election. The campaign marked the final ingredient in the opening of a critical “policy window” through which dramatic changes in land‐use policy could be realized. The Ontario case underlines the utility of adaptive models of policy making to the study of environmental policy, but suggests that these models perhaps underemphasize the desire of politicians and political parties to pursue policies in their electoral interest. El Plan de Lugares de Ontario para Crecer, publicado en 2006, señala el intento más audaz para hacer frente a la expansión urbana en Canadá, y quizás en toda Norteamérica. Entre sus muchos componentes, el plan establece un cinturón verde permanente que cubre aproximadamente 1.8 millones de acres (728,000 hectáreas) de tierra ambientalmente sensible. Este artículo busca explicar los cálculos políticos y las causas que llevaron a dicha política. Argumento que el plan fue parcialmente ideado para reunir apoyo de distritos suburbanos electorales clave a través del Área Metropolitana de Toronto en la elección provincial de 2003. La campaña marcó el ingrediente final en la apertura de una “ventana política” crítica que permitió cambios dramáticos en la política de uso de suelo. El caso de Ontario subraya la utilidad de modelos adaptativos de hechura de políticas en el estudio de la política ambiental, pero sugiere que quizás estos modelos subestiman el deseo de los políticos y los partidos de buscar políticas que los beneficien electoralmente.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".