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Record W4408286000 · doi:10.1111/anti.70011

Taking Settler Colonialism Seriously in Abolition Ecologies: Centring Indigenous Dispossession in Geographies of Carceral Power, Ecocide, and the Abolitionist Ecological Imagination

2025· article· en· W4408286000 on OpenAlexfundno aff
Kyla Simone Piccin

Bibliographic record

VenueAntipode · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEconomic and Social Research CouncilGirton College, University of Cambridge
KeywordsCentringIndigenousColonialismPower (physics)SociologyGender studiesEcologyPolitical scienceLawArt

Abstract

fetched live from OpenAlex

Abstract Scholarship increasingly examines international social movements advocating for the abolition of the prison‐industrial complex. Within this landscape, Abolition Ecologies has emerged as a generative intellectual space for examining the intersections of carceral power, environmental exploitation, and racial‐capitalist violence. However, there are opportunities to address the material dynamics of settler coloniality and Indigenous dispossession in this literature. Amid debates concerning the compatibility between abolition and anti‐colonialism, this article asks: What insights emerge when we centre Indigenous dispossession and settler coloniality in Abolition Ecologies? How might these insights complicate how solidarity is conceptualised and activated in the literature? This article identifies three under‐explored frictions that arise in centring Indigenous dispossession and settler colonialism in Abolition Ecologies. These frictions reveal complex challenges for the field. However, this article ultimately argues that Abolition Ecologies offers creative analytical and methodological tools to engage with these frictions. Rather than foreclosing solidarity, these frictions spark new opportunities for analysis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.322
Teacher spread0.313 · 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.

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

Citations4
Published2025
Admission routes1
Has abstractyes

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