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Record W4391363634 · doi:10.22514/jomh.2024.003

The level of the aggression in karate athletes with different handedness and belt grades

2024· article· en· W4391363634 on OpenAlexaff
Yaser Alikhajeh, Maghsoud Nabilpour, Mozhgan Ghollasimood, Fatma Hilal Yağın, Burak Yagin, Mehmet Gülü

Bibliographic record

VenueJournal of Men s Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsAthletesAggressionPsychologyDevelopmental psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Karate athletes with different lateral talents possess different functions in terms of skills and personality characteristics in a way that handedness can be considered an advantage. Given that there is a paucity of research in the domain of personality characteristics, handedness and belt grades, the current research aims to investigate the relationship between handedness and belt grades with aggression among karate athletes. 120 male karate athletes participated. To measure handedness, we used Annette’s handedness questionnaire and to measure aggression, we used Bredemeier’s aggression questionnaire. The questionnaires were distributed among participants one day before the tournament. A two-way analysis of variance (ANOVA) was used to measure the effects of belt grades and handedness on the level of aggression. The results of the study indicated that there was no statistically significant difference in the average level of aggression between left-handed and right-handed karate athletes. There was also no statistically significant difference in the average level of aggression between karate athletes with different belt grades.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.182
GPT teacher head0.479
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 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
Published2024
Admission routes1
Has abstractyes

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