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Record W4405098602 · doi:10.22215/etd/2024-16266

Collective Care for Carceral Transformation: Investigating Mutual Aid's Role in Building Decarceral Futures

2024· dissertation· en· W4405098602 on OpenAlexaff
Emilie Waters

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsFutures contractMutual aidTransformation (genetics)Political scienceSociologyBusinessLawFinanceChemistry

Abstract

fetched live from OpenAlex

This thesis examines how grassroots anti-carceral movements utilize mutual aid to support communities and advance abolitionist praxis.Drawing upon phenomenological interviews conducted with 13 participants who have either provided or received mutual aid within grassroots organizations, it examines the multifaceted role of mutual aid across three analytical chapters.The first chapter delves into participants' conceptualizations of mutual aid as community-driven, its capacity to challenge dominant support structures, and its role in fostering social change.The subsequent chapter investigates mutual aid's alignment with abolitionist politics, presenting it as a vehicle for offering alternative conflict resolution methods.Lastly, the final chapter confronts the challenges encountered within social movements and mutual aid projects, advocating for transformative justice integration as a solution.This research deepens our understanding of mutual aid within anti-carceral movements, underscoring its critical importance as both an essential element of abolitionist praxis and an alternative support system.

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)0.008
metaresearch head score (Gemma)0.011
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 score0.013
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.022
Scholarly communication0.0070.005
Open science0.0010.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.343
Teacher spread0.326 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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