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Record W2095717236 · doi:10.1007/s40750-014-0003-3

An Examination of the Associations Between Facial Structure, Aggressive Behavior, and Performance in the 2010 World Cup Association Football Players

2014· article· en· W2095717236 on OpenAlexaff
Keith M. Welker, Stefan Goetz, Shyneth Galicia, Jordan Liphardt, Justin M. Carré

Bibliographic record

VenueAdaptive Human Behavior and Physiology · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsNipissing University
Fundersnot available
KeywordsFootballAssociation (psychology)AggressionPsychologyFootball playersReciprocity (cultural anthropology)AthletesDevelopmental psychologySocial psychologyGeographyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Previous research suggests that facial-width-to-height ratio (FWHR) predicts aggression, unethical behavior, and non-reciprocity of trust. One limitation of this research is that all samples originate from western countries. To overcome this limitation, the present study investigates the relationship between FWHR and performance among association football athletes involved in the 2010 World Cup representing 32 countries. Results indicated that across all 32 countries, the associations between FWHR and athletic performance varied depending on position. FWHR positively predicted fouls within midfielders and forwards, and goals and assists within forwards. Collectively, these findings demonstrate the associations FWHR has with athletic behavior and performance for the first time in a well-varied multinational sample.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.256
Teacher spread0.217 · 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

Citations32
Published2014
Admission routes1
Has abstractyes

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