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Record W4399060765 · doi:10.29173/mlj1047

Bill 210: The Highway Traffic Amendment Act (Bicycle Helmets)

2008· article· en· W4399060765 on OpenAlexaboutno aff
Lana Jackson

Bibliographic record

VenueManitoba Law Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsAmendmentTransport engineeringEngineeringLawPolitical science

Abstract

fetched live from OpenAlex

}.J cording to current Manitoba law, it is illegal to ride a bicycle on the sidewalk.Under s. 145 (8) of The Highway Traffic Act, all cyclists; 'ncluding children, are to ride only on public roads and highways with the other traffic, unless the rear diameter of the bicycle wheel is less than 410 mm.Common sense would seem to dictate that when sharing the roads with motor vehicles, a reasonable bicyclist would wear a helmet.Nevertheless, in Manitoba this is not the case.Despite a concerted government effort promoting bike helmet usage over the past 15 years, only 28% of Manitobans wear bike helmets. 1 In an attempt to substantially increase the use of bicycle helmets in Manitoba, Dr. Jon Gerrard, the MLA for River Heights, introduced legislation to the Manitoba Legislature with Itivate Members' Bill 210, The Highway Traffic Amendment Act (Bicycle Helmets).Essentially, Bill 210 required anyone riding a bicycle on a highway or bicycle path to wear a proper protective helmet.The following paper will examine the circumstances surrounding the Manitoba Legislature's consideration of Bill 210.Areas that will be explored are: the discussion of the bill in the Legislature, the debate surrounding whether or not mandatory bicycle helmet legislation is necessary, where Manitoba currently sits in terms of its need and support of helmet legislation contrasted with those who oppose it, how other provinces have dealt with this issue, and why Bill 210 ultimately did not pass in the Manitoba Legislature.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.290
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2008
Admission routes1
Has abstractyes

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