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Record W627593583 · doi:10.3138/9781487595630

Magistrates, Police, and People: Everyday Criminal Justice in Quebec and Lower Canada, 1764-1837

2006· book· en· W627593583 on OpenAlexaboutno aff
Donald Fyson

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2006
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal justiceCriminologyEconomic JusticePolitical sciencePower (physics)Relevance (law)LawTheory of criminal justiceIndex (typography)State (computer science)Sociology

Abstract

fetched live from OpenAlex

The role and function of criminal justice in a conquered colony is always problematic, and the case of Quebec is no exception. Many historians have suggested that, between the Conquest and the Rebellions (1760s-1830s), Quebec's 'Canadien' inhabitants both boycotted and were excluded from the British criminal justice system. Magistrates, Police, and People challenges this simplistic view of the relationship between criminal law and Quebec society, offering instead a fresh view of a complex accord. Based on extensive research in judicial and official sources, Donald Fyson offers the first comprehensive study of the everyday workings of criminal justice in Quebec and Lower Canada. Focussing on the justices of the peace and their police, Fyson examines both the criminal justice system itself, and the system in operation as experienced by those who participated in it. Fyson contends that, although the system was fundamentally biased, its flexibility provided a source of power for ordinary citizens. At the same time, everyday criminal justice offered the colonial state and colonial elites a powerful, though often faulty, means of imposing their will on Quebec society. This fascinating and controversial study will challenge many received historical interpretations, providing new insight into the criminal justice system of early Quebec

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0150.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 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

Citations18
Published2006
Admission routes1
Has abstractyes

Explore more

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