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Record W3010710061 · doi:10.1123/ijsc.2019-0097

Do Members of a Winning Soccer Team Engage in More Communication Than a Losing Team? A Single-Game Study of Two Competing Teams

2020· article· en· W3010710061 on OpenAlexaff
Colin D. McLaren, Kevin S. Spink

Bibliographic record

VenueInternational Journal of Sport Communication · 2020
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeneralizability theoryPsychologyTask (project management)CentralityTeam compositionInformation exchangeTeam effectivenessAthletesSocial psychologyApplied psychologyOutcome (game theory)Team sportKnowledge managementComputer scienceDevelopmental psychologyEngineering

Abstract

fetched live from OpenAlex

Emerging evidence suggests that team success is associated with communication among group members. This study built on those findings by examining the degree to which members on a winning ( n = 13) and a losing ( n = 13) men’s soccer team exchanged task-related information during a single head-to-head game. Social network analysis was used to compute athlete information exchange at the individual and team levels by asking players to identify the specific members with whom they exchanged information during the game. As hypothesized, athletes on the winning team had higher average individual degree centrality and higher network-density scores than athletes on the losing team. This indicates that individual members on the winning team exchanged task-related information with more of their teammates and, as a result, engaged in more collective information exchange as a team. While replication is necessary to increase generalizability, this study suggests a possible link between the degree that team members exchange information (at the individual and team level) and team performance outcome (i.e., win or loss).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.362
Teacher spread0.320 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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