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Record W2170133500 · doi:10.1177/0193723514533199

Examining Whiteness and Eurocanadian Discourses in the Canadian Red Cross’ Swim Program

2014· article· en· W2170133500 on OpenAlexaffabout
Kyle Rich, Audrey R. Giles

Bibliographic record

VenueJournal of Sport and Social Issues · 2014
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMainstreamDiversity (politics)White (mutation)Diversity trainingCultural diversityGender studiesSociologyPublic relationsSocial psychologyPsychologyPolitical scienceLawAnthropology

Abstract

fetched live from OpenAlex

Each year, over 1 million Canadians participate in the Canadian Red Cross’ (CRC) Swim Program. Despite the increasing importance of cultural diversity in Canadian society, the CRC has yet to incorporate diversity training for this program’s Water Safety Instructors (WSIs). Through the use of critical Whiteness theory and critical discourse analysis, in this article, we examine the program’s content to assess the ways in which, if at all, it reflects mainstream, Eurocanadian and Whiteness discourses. Our analysis revealed two dominant discourses: (a) all participants should perceive risk and demonstrate leadership like Whites/Eurocanadians, and (b) behaviors that reflect White/Eurocanadian beliefs are normal and/or superior to other alternative ways of behaving. As a result of these findings, we suggest that future research should evaluate the possibility of implementing cultural safety training to equip instructors with a suitable understanding the cultural implications of aquatics programming, which may improve the program’s effectiveness for diverse Canadian populations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.988

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 studies0.0000.000
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 head0.038
GPT teacher head0.371
Teacher spread0.333 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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