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Record W4404582797 · doi:10.3138/chr-2023-0013

Mobsters, Mounties, and Canadian Governmental Responses to Organized Crime, 1965–67

2024· article· en· W4404582797 on OpenAlexaffvenueabout
Chris Madsen

Bibliographic record

VenueCanadian Historical Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCanadian Forces College
Fundersnot available
KeywordsLaw enforcementCommissionPolitical scienceLawPoliticsGovernment (linguistics)Criminal justiceEconomic JusticeEnforcementOrganised crimeFederal lawPublic administrationPower (physics)CriminologySociologyLegislation

Abstract

fetched live from OpenAlex

Due to its secretive nature and propensity to spread if left unchecked, organized crime bedeviled politicians and law enforcement officials in North America. The popular mythology surrounding the gangster and mobster betrayed the harsh realities of illicit business activities geared towards exploitive profit. Canada was slow to appreciate that career criminals connected to mafia syndicates from the United States and their home-grown accomplices were present and active during the 1960s. The Royal Canadian Mounted Police/Gendarmerie royale du Canada took the lead in criminal intelligence and maintained close relations with American law enforcement counterparts at the federal level. Separate efforts in Quebec focused on investigation towards prosecution, pushed by a tough-on-crime justice minister. The two approaches towards the problem came up in discussions between federal and provincial authorities during meetings in July 1965 and January 1966, interspersed by a federal election that returned a Liberal minority government to power. This article argues that the federal government’s preference for a modest law enforcement solution stymied real progress and instead served political expediency to dodge a demanded royal commission. Organized crime continued to flourish despite governmental efforts.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes3
Has abstractyes

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