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Record W3168017133 · doi:10.1111/anti.12735

Modulating Eventfulness: How Liaison Policing Strategies Mitigate Potentiality in Indigenous Land Defence Organising

2021· article· en· W3168017133 on OpenAlexafffundabout
Paul Sylvestre

Bibliographic record

VenueAntipode · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousColonialismSociologyCorporate governanceAgency (philosophy)Political sciencePublic administrationLawManagementSocial scienceEcology

Abstract

fetched live from OpenAlex

Abstract Liaison policing strategies are increasingly deployed as Canada’s frontline response to Indigenous‐settler land disputes. A fusion of pre‐emptive interventions, intelligence gathering, and best practices in public order and community policing, contemporary liaison strategies have important implications for Indigenous self‐determination and decolonial struggles. Yet, despite their rapid proliferation, we know little about how liaison strategies are enrolled as a technique of settler colonial governance. I begin addressing this gap through a four‐year case study tracking how a multi‐agency liaison policing assemblage undermined an urban Indigenous land reclamation in the city of Ottawa, Canada’s national capital. I show how liaison strategies worked by constraining the terrain of manoeuvre for radical organising while simultaneously facilitating Indigenous engagement in state‐sanctioned processes of recognition and accommodation. Arguing that the literature on public order policing inadequately theorises the relationship between liaison policing and settler colonial power, I propose “modulating eventfulness” as a more apposite conceptual grammar.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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.

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

Citations7
Published2021
Admission routes3
Has abstractyes

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