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Record W2055713516 · doi:10.1142/s1464333210003632

A POLICY WINDOW OPENS: STRATEGIC ENVIRONMENTAL ASSESSMENT IN YORK REGION, ONTARIO, CANADA

2010· article· en· W2055713516 on OpenAlexaffabout
Denis Kirchhoff, Dan McCarthy, Debbe D. Crandall, Laura McDowell, Graham Whitelaw

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsQueen's UniversityRegional Municipality of NiagaraUniversity of Waterloo
Fundersnot available
KeywordsStrategic environmental assessmentSustainabilityVariety (cybernetics)Government (linguistics)Plan (archaeology)Public administrationPublic policyEnvironmental policyPoliticsEnvironmental planningPolitical scienceField (mathematics)Strategic planningLocal governmentWindow of opportunityEnvironmental impact assessmentEngineeringManagementEconomicsGeographyComputer science

Abstract

fetched live from OpenAlex

Government agenda setting has been a focus of research in the field of policy sciences for over two decades. The concept of a policy window is explored as a driver of governmental agenda setting. The Regional Municipality of York, Ontario, Canada was chosen as a case study for exploring the application of strategic environmental assessment at the municipal level through a policy window lens. Problem, policy and political streams converged to provide the necessary conditions for improved environmental assessment and infrastructure planning in York Region. A focusing event and the resulting crisis motivated stakeholders to identify and act on the problem. An SEA-type approach was initiated as one key response. A variety of activities were initiated by York Region including the development of a Sustainability Strategy, synchronisation of master planning, wider consideration of alternatives at the master plan level and improved public consultation. Conclusions are drawn and several recommendations are presented and discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 spread0.254 · 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

Citations10
Published2010
Admission routes2
Has abstractyes

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