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Record W1986407503 · doi:10.1177/1012690214526878

An unexceptional exception: Golf, pesticides, and environmental regulation in Canada

2014· article· en· W1986407503 on OpenAlexaffabout
Brad Millington, Brian Wilson

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLegislationMandateSustainabilityEnvironmental lawLawPolitical scienceBusinessEnvironmental ethicsEnvironmental protectionGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.266
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2014
Admission routes2
Has abstractyes

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