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Record W4411354003 · doi:10.1017/cls.2025.1

Tracking (In)Justice: Documenting Fatal Encounters with Police in Canada

2025· article· en· W4411354003 on OpenAlexaffabout
Andrew Crosby, Alexander McClelland, Tanya L. Sharpe, Evelyn M. Maeder, Catherine Stinson, Kanika Samuels Wortley, Rafiullah Khan

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsRoyal Canadian Mounted PoliceCanadian Criminal Justice AssociationQueen's UniversityUniversity of TorontoSunnybrook Health Science CentreCarleton University
Fundersnot available
KeywordsCriminologyEconomic JusticeTracking (education)PsychologyPolitical scienceComputer securityComputer scienceLawPedagogy

Abstract

fetched live from OpenAlex

Abstract There is a lack of knowledge on deaths related to police use of force across Canada. Tracking (In)Justice is a research project that is trying to make sense of the life and death outcomes of policing through developing a collaborative, interdisciplinary, and open-source database using publicly available sources. With a collaborative data governance approach, which includes communities most impacted and families of those killed by police, we document and analyze 745 cases of police-involved deaths when intentional force is used across Canada from 2000 to 2023. The data indicate a steady rise in deaths, in particular shooting deaths, as well as that Black and Indigenous people are over-represented. We conclude with reflections on the ethical complexities of datafication, knowledge development of what we call death data and the challenges of enumerating deaths, pitfalls of official sources, the data needs of communities, and the living nature of the Tracking (In)Justice project.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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