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Record W2135663016 · doi:10.1177/1046496404267942

Cohesion as Shared Beliefs in Exercise Classes

2005· article· en· W2135663016 on OpenAlexaff
Shauna M. Burke, Albert V. Carron, Michelle M. Patterson, Paul A. Estabrooks, Jennie L. Hill, Todd M. Loughead, S. R. Rosenkranz, Kevin S. Spink

Bibliographic record

VenueSmall Group Research · 2005
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of SaskatchewanMcGill UniversityWestern University
Fundersnot available
KeywordsCohesion (chemistry)Group cohesivenessPsychologySocial psychologyTask (project management)PerceptionGroup (periodic table)Applied psychology

Abstract

fetched live from OpenAlex

The purpose of the study was to determine if perceptions of cohesion in exercise classes demonstrated sufficiently high consensus and between-group variance to support a conclusion that exercise classes are groups. Participants (N = 1,700) in 130classes were tested on either the Group Environment Questionnaire (GEQ) or the Physical Activity Group Environment Questionnaire (PAGEQ). Results showed that exercise classes satisfied the statistical criteria necessary to support a conclusion that they are true groups; that is, they exhibited acceptable levels of consensus about cohesion within classes and acceptable differences in cohesion between classes. In addition, index-of-agreement values were significantly greater for participants completing the PAGEQ than for participants completing the GEQ. Finally, consensus was greatest when participants evaluated how the exercise class satisfied their own personal task needs (i.e., individual attractions to the group-task), and second greatest when participants evaluated the collective unity around the task objectives (i.e., group integration-task).

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)0.005
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.263
GPT teacher head0.515
Teacher spread0.252 · 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 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

Citations21
Published2005
Admission routes1
Has abstractyes

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