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Record W2772139495 · doi:10.1017/cls.2017.25

Criminal Justice Policy during the Harper Era: Private Member’s Bills, Penal Populism, and the Criminal Code of Canada

2017· article· en· W2772139495 on OpenAlexaffabout
James B. Kelly, Kate Puddister

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of GuelphConcordia University
Fundersnot available
KeywordsScrutinyLegislationParliamentCriminal justicePolitical scienceEconomic JusticeLawGovernment (linguistics)Criminal codeCriminal lawPublic administrationLaw and economicsSociologyPolitics

Abstract

fetched live from OpenAlex

Abstract During the 2015 Maclean’s election debate, Stephen Harper commented, “we have more private member’s legislation that has gone through Parliament under this government than multiple governments before us.” This statement is borne out by empirical evidence: more private member’s bills (PMBs) have become law during Harper’s time in government, compared with most previous parliaments. However, PMBs are subject to less analysis than government bills and do not receive legal scrutiny by the Department of Justice, potentially implicating the protection of rights. Moreover, while one might assume that PMBs concern innocuous local and/or specialized interests, many Harper era PMBs effect substantive legal change to national issues like criminal justice policy. This paper examines the law and order trend in PMBs and addresses the following: why would the PMO under Stephen Harper, noted for its centralized control over all aspects of public policy, permit backbench MPs a role in criminal justice policy, through PMBs?

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
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.004
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.284
Teacher spread0.256 · 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 designTheoretical or conceptual
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

Citations10
Published2017
Admission routes2
Has abstractyes

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