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Record W4399992207 · doi:10.1080/14747731.2024.2366326

‘We will continue to fight for our lands … it is Mother Nature that we value’: Idle No More, the Rights of Nature social movement frame, and Anti-Capitalist Ecologist Discourse

2024· article· en· W4399992207 on OpenAlexaboutno aff
Julie Schweitzer, Tamara L. Mix, Olivia M. Fleming

Bibliographic record

VenueGlobalizations · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Movement (music)Social movementFrame (networking)Political economyIdleEnvironmentalismEnvironmental movementPolitical scienceSociologyEconomic systemMarket economyEnvironmental ethicsLaw and economicsEconomicsLawPoliticsEngineeringAestheticsTelecommunications

Abstract

fetched live from OpenAlex

Organizing to oppose an omnibus budget bill threatening First Nations in Canada, the Idle No More (INM) movement is embedded in a legacy of Indigenous resistance to colonialism and environmental degradation. INM activists challenged Western nature perceptions to advocate for the protection of nature through the lens of First and Indigenous Peoples’ traditions and cultures. We build upon existing Indigenous Environmental Justice movement, framing, and Rights of Nature (RoN) literature to understand the role of the RoN social movement frame and Anti-Capitalist Ecologist Discourse as orienting frameworks for INM movement mobilization. We explore the emergence period of INM, analysing the first 6 months of posts and comments on the movement’s Facebook page. We argue that INM activists employed an RoN frame to establish central claims and appeal to a range of potential supporters connecting the local and the global through broad Anti-Capitalist Ecologist Discourse narratives.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.699
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.032
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.004
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 head0.013
GPT teacher head0.354
Teacher spread0.341 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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