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Record W4283591099 · doi:10.1080/1091367x.2022.2092740

Part of the Team: The Social Identity Questionnaire for Sport Parents (SIQS-P)

2022· article· en· W4283591099 on OpenAlexaff
Jordan Sutcliffe, Alex J. Benson, Colin D. McLaren, Mark W. Bruner

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsCape Breton UniversityNipissing UniversityWestern University
Fundersnot available
KeywordsPsychologyIngroups and outgroupsSocial psychologySocial identity theoryIdentity (music)Confirmatory factor analysisDevelopmental psychologySport psychologyPerceptionTeam sportSocial cognitionSocial cognitive theoryCognitionStructural equation modelingAthletesSocial group

Abstract

fetched live from OpenAlex

Drawing from theory of the multidimensional nature of social identity, the purpose of this study was to assess an adapted measure of social identity in sport that captures the extent to which parents identify with their child’s team. Using the Social Identity Questionnaire for Sport (SIQS) with items specifically modified for parents, we assessed competitive youth sport parents’ perceptions of ingroup ties, cognitive centrality, and ingroup affect. Using an exploratory and confirmatory analysis process, a conceptually grounded three-factor structure was supported in a sample of 788 ice hockey parents. Measurement invariance testing found strong invariance for the measurement tool based on participants’ biological sex, previous sport experience, and proximal competition outcome. Finally, differences emerged when comparing latent means across their child’s most recent game outcome. Taken together, this study offers a novel tool to better understand and measure perceptions of social identity among sport parents.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.044
GPT teacher head0.374
Teacher spread0.330 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMeasurement in Physical Education and Exercise ScienceSame topicSport Psychology and PerformanceFrench-language works237,207