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Record W1978984779 · doi:10.1080/10413200.2011.607486

Team Attributions in Sport: A Meta-Analysis

2012· article· en· W1978984779 on OpenAlexaff
Luc J. Martin, Albert V. Carron

Bibliographic record

VenueJournal of Applied Sport Psychology · 2012
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsWestern University
Fundersnot available
KeywordsLuckPsychologyAttributionMeta-analysisDimension (graph theory)Applied psychologyTask (project management)Team sportSocial psychologyScale (ratio)Cognitive psychologyAthletesManagement

Abstract

fetched live from OpenAlex

The purpose of the study was to determine (via meta-analysis) whether team-oriented attributions in sport are team-serving. Analyses were carried out with 21 studies containing 99 effect sizes. The results indicated (a) the presence of a team-serving bias, (b) no temporal change in the pattern of results from studies using operational definitions based on Weiner's theoretical model, and (c) significant differences (p < .05) between the internal composite dimension (i.e., ability/effort versus task difficulty/luck) advanced by Weiner (1985) and the internal dimensions developed by Greenlees et al. (2005; Causal Dimension Scale for Teams). This study documents the continued presence of the team-serving bias.

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)0.031
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.064
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.039
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.075
GPT teacher head0.372
Teacher spread0.297 · 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 designMeta-analysis
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

Citations17
Published2012
Admission routes1
Has abstractyes

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