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Record W4411557143 · doi:10.3390/youth5030062

Navigating Complexity: Ethical and Methodological Insights from a Trauma-Informed Participatory Action Research Study with Young People in Sport for Development

2025· article· en· W4411557143 on OpenAlexafffund
Julia Ferreira Gomes, Isra Iqbal, Lyndsay Hayhurst

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsYork University
FundersYork University
KeywordsParticipatory action researchReflexivityAction researchCitizen journalismEngineering ethicsAction (physics)SociologyPsychologyPublic relationsPolitical scienceSocial sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

Participatory action research (PAR) has been increasingly used in sport for development research due to its potential to challenge hegemonic forms of knowledge production within sport contexts. Drawing on the youth and feminist action literature, we explore the methodological and ethical challenges of conducting participatory research as young academic researchers collaborating with young coaches as community collaborators. This article calls for greater transparency in how researchers conduct YPAR, whether it is youth-centred or youth-led, and underscores the utility of a feminist lens and trauma- and violence-informed framework in grounding critical reflexivity throughout the research process. These contributions aim to advance ethically grounded, trauma-informed action research projects with young people in sport and physical activity settings.

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)0.177
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1770.152
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0300.063
Scholarly communication0.0270.013
Open science0.0040.027
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0020.000

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 head0.609
GPT teacher head0.555
Teacher spread0.053 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2025
Admission routes2
Has abstractyes

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