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Record W2030601355 · doi:10.1089/env.2012.0003

Losing Its Way: Environmental Impact Assessment in British Columbia, Canada

2012· article· en· W2030601355 on OpenAlexaffabout
Bruce R. Muir, Annie L. Booth

Bibliographic record

VenueEnvironmental Justice · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSubsistence agricultureEnvironmental impact assessmentThreatened speciesGovernment (linguistics)IndigenousEnvironmental justiceEnvironmental planningGeographyScope (computer science)Settlement (finance)Political scienceNatural resourceEnvironmental resource managementEnvironmental protectionBusinessLawArchaeologyEcologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

Environmental impact assessment is viewed as an integral planning tool with respect to the sustainable development of land and natural resources, as it has the potential to protect the different values held by individuals and groups when done appropriately. This article examines an approach by the Environmental Assessment Office of British Columbia, Canada, regarding the scope of a cumulative effect assessment for the environmental assessment process of a proposed coal mine project that is endangering a threatened herd of caribou relied upon by West Moberly First Nations (an Indigenous group in Canada) for cultural subsistence. A Canadian-based equality framework is used to ground the environmental justice analysis. We conclude that the government's application of its discretionary powers in this case resulted the cultural values of West Moberly being given a diminished level of protection and benefit of the law in comparison to the social values held by mainstream society.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0080.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2012
Admission routes2
Has abstractyes

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