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Record W3026076981 · doi:10.25035/ijare.12.04.08

Promising Practices for Boating Safety Initiatives that Target Indigenous Peoples in New Zealand, Australia, the United States of America, and Canada

2020· article· en· W3026076981 on OpenAlexaffabout
Mitchell Crozier, Audrey R. Giles

Bibliographic record

VenueInternational Journal of Aquatic Research and Education · 2020
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIndigenousOccupational safety and healthEnvironmental planningGeographyCultural safetyWater safetyEnvironmental resource managementPolitical scienceEcology

Abstract

fetched live from OpenAlex

Boating-related incidents are responsible for a significant number of the drowning fatalities that occur within Indigenous communities in New Zealand, Australia, the USA, and Canada. The aim of this paper was to identify promising practices for boating safety initiatives that target Indigenous peoples within these countries and evaluate past and ongoing boating safety initiatives delivered to/with Indigenous peoples within these countries to suggest the ways in which they – or programs that follow them - may be more effective. Based upon evidence from previous research, boating safety initiatives that target Indigenous peoples in New Zealand, Australia, the USA, and Canada should employ cultural adaptation strategies, strategies to increase boating safety knowledge and awareness, strategies to increase the accessibility of boating safety equipment, and capacity building strategies. Improvements can be made to past, ongoing, and future boating safety initiatives delivered to/with Indigenous peoples in the four countries studied. These strategies all show promise in improving boating safety initiatives and decreasing boating-related drowning.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.140
GPT teacher head0.464
Teacher spread0.325 · 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 designObservational
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

Citations2
Published2020
Admission routes2
Has abstractyes

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