Taking Settler Colonialism Seriously in Abolition Ecologies: Centring Indigenous Dispossession in Geographies of Carceral Power, Ecocide, and the Abolitionist Ecological Imagination
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".