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Record W4396909318 · doi:10.1139/facets-2023-0118

Access to Environmental Justice in Canadian environmental impact assessment

2024· article· en· W4396909318 on OpenAlexaffvenueabout
Thomas Gilmour, Jocelyn Stacey

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental justiceEnvironmental impact assessmentEnvironmental planningPolitical scienceEnvironmental resource managementEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

Contemporaneous reforms to Canada and British Columbia's environmental impact assessment legislation have the potential to advance Access to Environmental Justice. Access to Environmental Justice is the ability of individuals and communities who are disproportionately and negatively impacted by environmental decisions to access legal and regulatory processes and to have their concerns heard and addressed through environmental decision-making and dispute resolution. Access to Environmental Justice connects concepts of environmental justice, public participation, the rule of law, and access to justice to provide a framework for evaluating the implementation of environmental impact assessment laws. We conducted a preliminary analysis of early implementation of legislative reforms in Canada and British Columbia. Our analysis indicates that a number of factors influence who is seeking to access environmental justice through environmental impact assessment, including geography, project type, and the availability of a legislative mechanism that allows anyone to request an assessment. Whether Canada and British Columbia's reforms are advancing Access to Environmental Justice requires continued analysis as projects continue to be assessed under the new laws.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.007

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 head0.343
Teacher spread0.329 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2024
Admission routes3
Has abstractyes

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