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Record W2943308644

Rip Currents Within the Canadian Curriculum

2018· article· en· W2943308644 on OpenAlexaboutno aff
Summer Locknick

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPolitical scienceHistorySociologyPedagogy
DOInot available

Abstract

fetched live from OpenAlex

With 32 recorded drownings related to rip currents in the Great Lakes in 2017, rip currents are a public health hazard to North American beach users. The purpose of this study is to examine the Canadian curriculum through the Curriculum Documents provided by the Ministry of Education, to observe if and how beach hazards are taught to students 18 and under. This study will also analyze a survey directed to Ontario teachers, about whether they believe there is a need for beach hazards to be incorporated into the curriculum. The primary focus will be the Ontario curriculum, as Ontario citizens are more at risk of drowning in the Great Lakes due to their proximity. Initial evidence from surveys done in 2016 by the University of Windsor suggests that beach users have limited knowledge of rip currents, partially due to the lack of education surrounding beach hazards. Results suggested that the national Canadian curriculum does not incorporate rip current knowledge and many other beach hazards into their lesson plans. However, two programs on water safety are offered to Ontario schools sponsored by the National Lifesaving Society. These program’s primary objective is to teach children in grades three and seven how to roll, tread, and swim in deep water. These programs do not incorporate beach hazards in classroom discussions and are not mandatory for students. The goal of this paper is to draw attention to the need for beach hazards to be taught in the classroom to reduce the number of future drownings. Key words: Rip current, Canadian Curriculum, Great Lakes, drownings

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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; a candidate call from one teacher head, not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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