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Record W4396507585 · doi:10.4324/9781003395867-18

Centering Indigenous voices and experiences to advance positive youth development in sport research

2024· book-chapter· en· W4396507585 on OpenAlexaboutno aff
Leah J. Ferguson, Tara-Leigh McHugh, Leisha Strachan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPsychologySociologyPolitical scienceBiologyEcology

Abstract

fetched live from OpenAlex

A well-established body of research shows that sport can be an avenue for positive youth development (PYD). However, consistent with general trends in the vast sport literature, much of the PYD-in-sport research has been generated within a western, colonial context, and the experiences of Indigenous peoples 1 are often overlooked and underrepresented. To advance PYD-in-sport research, it is critical to center Indigenous voices and experiences in such work. Indigenous youth are the fastest-growing cohort of youth in Canada, with the Indigenous population growing at twice the pace of the non-Indigenous population ( Statistics Canada, 2022 ). Indigenous youth continue to demonstrate resilience as they navigate the effects of settler colonialism that have led to unique structural inequities in all facets of life, including sport opportunities. Within the context of Canada, Indigenous youth are “key drivers” of social outcomes ( Canadian Heritage, 2021 , p. 12), and their voices and experiences must play a critical role in advancing sport research, programming, and policy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.012
Scholarly communication0.0080.007
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.057
GPT teacher head0.361
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
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
Published2024
Admission routes1
Has abstractyes

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