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Record W2735782025

Social identity and the influence on golfing performance

2011· article· en· W2735782025 on OpenAlexaff
Anthony GVander Laan, Todd M. Loughead, Krista J. Munroe‐Chandler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyAthletesIdentity (music)Multivariate analysis of varianceSocial psychologySocial identity theoryNormativeIdentification (biology)StatisticSignificant differenceRanking (information retrieval)Rank (graph theory)Social groupMathematicsStatisticsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Social identity theory states that individuals will define themselves in terms of their group memberships (Haslam, 2004). The theory asserts that individuals will begin to adopt normative group characteristics through a process of depersonalization (Hogg & Terry, 2000). While research has been conducted with respect to fan identification in sport, little research has been done with athletes (Branscombe & Wann, 1991). Therefore, the purpose of the current study was to examine which athletes benefit the most from team identification. Participants (N = 155) were professional golfers from the PGA and LPGA tours who have competed in both individual match play tournaments and team based match play tournaments. Participants were classified into either a higher or lower golfing ability condition based on their lifetime World Golf Ranking statistic. Results of a MANOVA indicated an overall difference between higher and lower golfing ability, F(4,150) = 2.59, p = .039, ?2 = .065. In particular, the results showed a significant difference between higher ability golfers and lower ability golfers in relation to individual match play, but no significant difference in team match play. Results are discussed in terms of how performance within a team setting is facilitated due to social identity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.474

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 science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.227
Teacher spread0.160 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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