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Re(con)figuring Alliances: Place Membership, Environmental Justice, and the Remaking of Indigenous-Environmentalist Relationships in Canada's Boreal Forest

2012· article· en· W2011000737 on OpenAlexaboutno aff
Anna J. Willow

Bibliographic record

VenueHuman Organization · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEnvironmental ethicsPoliticsEnvironmentalismContext (archaeology)AllianceEnvironmental justiceSociologyClimate justicePolitical scienceEcologyLawGeographyClimate change

Abstract

fetched live from OpenAlex

Critical observers of the international environmental movement have found that indigenous-environmentalist alliances have often been predicated upon reproductions of an asymmetrical political status quo, thereby perpetuating indigenous peoples' systemic disadvantages and predestining promising partnerships for eventual disintegration. Spotlighting the relationship between Grassy Narrows First Nation and Rainforest Action Network, this article describes how indigenous-environmentalist alliances are being constructively re(con)figured in the context of recent anti-clearcutting activism in northwestern Ontario. An analysis of the positive interpersonal relationships cited by participants as key to the coalition's success reveals the significance of (1) a social setting conducive to imagining membership in a diverse community united by an emplaced interest in boreal forest protection and (2) a transformed conceptual framework that redefines the environment to include human activities and concerns. I argue that this dynamic socio-discursive context not only facilitated the development of a strong alliance and an effective conservation campaign, but may also ultimately empower indigenous communities to participate in environmental protection on terms that are closer to their own. When environmentalists refigure the categories that guide their relationships to the places they seek to protect, they also reconfigure the power structures underpinning their alliances with the indigenous groups who call those places home.

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0330.020
Scholarly communication0.0050.002
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.248
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 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

Citations21
Published2012
Admission routes1
Has abstractyes

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