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Record W2907377689 · doi:10.29173/cjs29397

Strategic Incapacitation of Indigenous Dissent: Crowd Theories, Risk Management, and Settler Colonial Policing

2018· article· en· W2907377689 on OpenAlexaffvenueabout
Miles Howe, Jeffrey Monaghan

Bibliographic record

VenueThe Canadian Journal of Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsCarleton UniversityQueen's University
Fundersnot available
KeywordsDissentIndigenousOperationalizationSociologyImpartialityScholarshipCriminal justiceCriminologyObjectivity (philosophy)ColonialismRationalityLawPublic relationsPolitical sciencePoliticsEpistemology

Abstract

fetched live from OpenAlex

Engaging scholarship from sociologies of security to protest policing, this article explores how risk management and actuarial tools have been operationalized in Canadian policing of Indigenous protests. We detail RCMP actuarial tools used to assess individual and group risk by tracing how these techniques are representative of much older trends in the criminal justice system surrounding the management of risk, but also have been advanced by contemporary databanking and surveillance capacities. Contesting public claims of police impartiality and objectivity, we highlight how the construction of riskiness produces an antagonism towards “successful” Indigenous protests. Though the RCMP regularly claim to “protect and facilitate the right to lawful advocacy, protest and dissent,” we show how these practices of strategic incapacitation exhibit highly antagonistic forms of policing that are grounded in a rationality that seeks to demobilize and delegitimize Indigenous social movements.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0080.043
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.342
Teacher spread0.302 · 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

Citations22
Published2018
Admission routes3
Has abstractyes

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