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Record W2030648090 · doi:10.1136/ip.2010.029215.320

Vancouver Charter: bringing ski and snowboard helmet legislation to Canada

2010· article· en· W2030648090 on OpenAlexaffabout
P Fuselli, Brent Hagel, Richard Stanwick

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsActive Healthy KidsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsCharterContext (archaeology)LegislationRecreationOccupational safety and healthPolitical scienceCommissionPublic administrationPublic relationsLawHistory

Abstract

fetched live from OpenAlex

The Vancouver Charter on Skiing Safety is based on skiing and snowboarding safety and the Turin Charter (approved in the context of the 2006 Turin Winter Olympic Games) and was brought to Canada by Safe Kids Canada. Originally prepared by a panel of experts and specialists delegated by European Governments who worked under the coordination of TOROC (Turin Organizing Committee), BE.PRA.S.A. (a project co-financed by the European Commission and the health authority of the Italian region of Veneto) and the Italian National Health Institute, the aim of the Charter is to promote a safe, healthy and active sporting and recreational culture, and to make its principles visible to the public, citizens and institutions. Over 30 international, national, provincial and municipal organisations have endorsed the Charter to date. The Charter was introduced at a Helmet Forum in November 2009 in Vancouver and featured experts such as Dr Charles Tator, Dr Richard Stanwick, Dr Brent Hagel and Mr Matt Herman. The forum was attended by injury prevention experts, ski/snowboard industry representatives and media and resulted in extensive national media coverage of the issue, especially the diversity of opinions and debates it has created. This presentation will show Safe Kids Canada's experience of how the Charter can be leveraged by other countries, review the challenges and successes, present our knowledge translation ‘recipe for success’ and update the audience on activities to date.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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
Published2010
Admission routes2
Has abstractyes

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