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Record W4221019332 · doi:10.1111/cag.12754

Exposure, access, and inequities: Central themes, emerging trends, and key gaps in Canadian environmental justice literature from 2006 to 2017

2022· article· en· W4221019332 on OpenAlexafffundvenueabout
Amanda Giang, David R. Boyd, Aspen J. Ono, Bronwyn McIlroy‐Young

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of British Columbia
KeywordsEnvironmental justiceInjusticeRacializationEconomic JusticePolitical scienceScope (computer science)Environmental studiesSociologyEnvironmental ethicsEnvironmental planningGeographyPoliticsLaw

Abstract

fetched live from OpenAlex

Environmental injustices—in the form of inequitable distribution of environmental risks and benefits, uneven access to decision‐making processes, and misrecognition of communities—have been documented globally. However, in Canada, many have argued that the story of environmental injustice is less widely known, with more fragmented research that has produced little in terms of public policies intended to alleviate injustice. This paper uses a meta‐narrative review approach to map the evolution of environmental justice research in Canada between 2006‐2017, and characterize how central themes, emerging trends, and gaps in the literature have changed since the last review of this kind was completed. We conducted a systematic search of publications addressing environmental justice in Canada, yielding 820 publications. We coded abstracts to assess patterns of coverage across space, time, topics, and populations of focus. We find that Canadian environmental justice literature has continued to grow in quantity and scope, addressing more dimensions of environmental harms and benefits, and from an increasingly integrated perspective. However, there remain important and persistent gaps in its coverage. Future research that more fully addresses these geographic (e.g., Atlantic and Prairie regions), topical (e.g., focus on prevention), and recognitional (e.g., racialization) gaps is needed to inform policy‐making and promote justice .

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
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
Bibliometrics0.0040.003
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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
Published2022
Admission routes4
Has abstractyes

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