An unexceptional exception: Golf, pesticides, and environmental regulation in Canada
Bibliographic record
Abstract
This paper features a critical examination of recent legislation banning cosmetic pesticide applications in the province of Ontario, Canada. It focuses in particular on the exemption of golf courses from the province’s Cosmetic Pesticides Ban Act of 2009. Drawing from a wide range of materials, the authors first contextualize Ontario’s recent law through an overview of the historical development of pre- and post-market pesticide regulation in Canada. This includes a review of the fierce debates that have at times arisen between pro- and anti-chemical factions. From there, the authors evaluate the Cosmetic Pesticides Ban Act. In one sense, the law – and especially golf’s exemption from the law – is said to exemplify “environmental managerialist” decision-making, whereby governments must satisfy a “dual mandate” of promoting economic growth and environmental sustainability simultaneously. In another, related way, it is seen as demonstrative of an “ecological modernist” approach to environmental problems in which industry-led, technologically-advanced solutions are privileged above others. Taken together, golf’s “special status” in Ontario’s new pesticide legislation is deemed reflective of a wider trend towards neoliberal environmental policy making in Canada. It is also regarded in closing as a reason for future research into sport and environmental policy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".