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Record W1746476984

Policing with Impunity

2016· article· en· W1746476984 on OpenAlexaboutno aff

Bibliographic record

VenueSocialist register · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsImpunityAutonomyCoercion (linguistics)Public securitySpace (punctuation)SociologyLawSimple (philosophy)CriminologyPolitical sciencePolitical economyPoliticsComputer scienceEpistemology
DOInot available

Abstract

fetched live from OpenAlex

This essay emerges from a frustration with two common sense caricatures of police that circulate in left movements. The first portrays the police as the tools of elites, simple enforcers of an exploitative system. In this version, the police are not strategic actors, but merely the brute force of coercion – a force that is thuggish and often bumbling. We blur differences across time and space and suggest that any variation is solely a direct result of economic shifts. At other times, the police are portrayed as autonomous, even omnipotent, strategic players, without examining how relative their autonomy actually is, nor understanding how things might be changing and how and why police strategy varies across time and space. In fact, policing in Canada, the US and the UK is undergoing a broad shift in its logic and strategy. This shift is increasing police impunity, and involves an integration of the previously distinct fields of public and private policing, security and intelligence activities.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0160.056
Scholarly communication0.0130.009
Open science0.0010.013
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.069
GPT teacher head0.387
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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