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Record W4416347361 · doi:10.1111/caje.70025

Racial bias in criminal sentencing: Historical evidence from Chinese railway workers in British Columbia

2025· article· en· W4416347361 on OpenAlexafffundvenueabout
Kris Inwood, Ian Keay, Blair Long

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsMemorial University of NewfoundlandQueen's UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaMinnesota Population Center, University of Minnesota
KeywordsRecidivismCriminal justiceImmigrationContext (archaeology)Racial biasEconomic shortagePrison

Abstract

fetched live from OpenAlex

Abstract Do discriminatory attitudes held in the public influence public institutions? We study this question within the context of the criminal justice system of historical British Columbia (BC). During the late 1870s and early 1880s, an influx of Chinese immigrant workers employed in the building of the Canadian Pacific Railway (CPR) was the catalyst for a wave of anti‐Chinese sentiment in British Columbia (BC). We test for the presence of racial bias in criminal sentencing during this era by comparing marginal sentences for predicted recidivism risk across groups in a triple difference framework, using the building of the CPR as a plausibly exogenous shock to recidivism. We find that during the early years of the building boom, Chinese prisoners received sentences that were over twice as long as other observationally equivalent prisoners per unit of recidivism risk. Robustness exercises illustrate that this result is similar if we focus on the size of Chinese immigration instead of recidivism risk. We find that this sentencing bias is temporary, and dissipates towards the end of the project, coincident with labor shortages that threatened its viability.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.175
GPT teacher head0.237
Teacher spread0.062 · 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 designObservational
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

Citations0
Published2025
Admission routes4
Has abstractyes

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