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Record W3204465214 · doi:10.32866/001c.28345

Police Enforcement of Cycling Offences: A Case Study in Winnipeg, Manitoba

2021· article· en· W3204465214 on OpenAlexaffabout
Orly Linovski, Aaron Snider

Bibliographic record

VenueFindings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCensusEnforcementImmigrationLaw enforcementEquity (law)CriminologyUnavailabilityDemographic economicsGeographyPolitical scienceDemographySociologyLawEngineeringEconomicsPopulation

Abstract

fetched live from OpenAlex

Bias in policing is a concern in many Canadian cities, through there is little known about the impact on cyclists. This research analyzed the type and location of tickets issued to cyclists in Winnipeg, Manitoba to understand disparities in enforcement. We find that tickets are issued almost exclusively in inner-city areas, with the majority of tickets issued in five census tracts. Tickets were issued in areas that generally had more racialized and immigrant residents, and uniformly had more low-income residents. The unavailability of key data sources limits more in-depth understanding of the impact of policing on equity-seeking groups.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.035
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0180.003
Scholarly communication0.0020.001
Open science0.0030.004
Research integrity0.0020.002
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 head0.098
GPT teacher head0.401
Teacher spread0.303 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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