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Record W7095863307

Interlock Program Standards for Canada

2015· article· en· W7095863307 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInterlockLegislationEnforcementControl (management)Poison controlLaw enforcement
DOInot available

Abstract

fetched live from OpenAlex

program was primarily voluntary and involved relatively small numbers of drivers who had been convicted of an impaired driving offence. Offenders were offered a reduction in the length of their licence suspension if they participated in the interlock program. An evaluation of the program showed a substantial reduction in the number of subsequent impaired driving offences among those who participated in the interlock program (Beirness et al. 1997; Voas et al. 1999). In July 1999, the Criminal Code of Canada was amended to allow the court to reduce the mandatory period of driving prohibition for a first impaired driving offence from one year to three months provided the offender participated in an alcohol interlock program for the remainder of the original period of prohibition. Subsequent amendments allowed second offenders a reduction in the length of the driving prohibition if they participated in an interlock program. This legislation gave implicit federal approval to interlock programs and provided the impetus for provinces to renew interest in the development and/or expansion of such programs. Today, most provinces and territories have either implemented an ignition interlock program or have announced the intention to do so in the near future. Among the numerous interlock programs that have been implemented throughout the world,

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.067
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0100.002
Scholarly communication0.0080.003
Open science0.0060.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0650.014

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 head0.034
GPT teacher head0.278
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2015
Admission routes1
Has abstractyes

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