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The controversy of transferring the Class Environmental Assessment process to northern Ontario, Canada: the Victor Mine Power Supply Project

2011· article· en· W2027029384 on OpenAlexaffabout
Jessica McEachren, Graham S. Whitelaw, Daniel D. McCarthy, Leonard J. S. Tsuji

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsQueen's UniversityUniversity of Waterloo
Fundersnot available
KeywordsContext (archaeology)ArcticBayClass (philosophy)Environmental impact assessmentEnvironmental resource managementGovernment (linguistics)Operations researchEnvironmental protectionComputer scienceGeographyEnvironmental scienceArchaeologyPolitical scienceEngineeringOceanographyGeologyLawArtificial intelligence

Abstract

fetched live from OpenAlex

Since Canada employs a federated system of government, there are separate environmental assessment (EA) processes at the national and provincial levels. In the Province of Ontario there is a streamlined, pre-approved, self-assessed process for ‘classes’ of projects. It is assumed that Class EA protocol developed in the southern Ontarian context is directly transferable to northern Ontario. A case-based approach, using the Victor Mine transmission line project, was employed to critically examine whether the Class EA template developed in southern Ontario should be applied to the western James Bay region of northern Ontario. Specifically, the two assumptions of Class EAs of predictability and manageability were examined. Interview and document data were used to inform a themed analysis. Results indicate that the western James Bay region is significantly different to southern Ontario. Thus, the Class EA template developed in and for southern Ontario is not transferable to the northern Ontarian context and the application of ‘cookie cutter’ EAs to other sub-arctic and arctic regions must be questioned.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.314
Teacher spread0.298 · 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

Citations11
Published2011
Admission routes2
Has abstractyes

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