MétaCan
Menu
Back to cohort
Record W1942787529

The comparison of mental skills of elite male and female chess players in Iran.

2014· article· en· W1942787529 on OpenAlexaboutno aff
Majid Keramati Moghadam, Afsaneh Sanatkaran, Seyyed Mohialdin Bahari

Bibliographic record

VenueInternational Journal of Sport Culture and Science · 2014
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMultivariate analysis of variancePsychologyEliteSignificant differenceDescriptive statisticsCognitionMental healthApplied psychologyMedicinePsychiatryStatistics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the elite male and female chess players, mental skills in Iran. The statistical community of this present study include the active and Iranian women and men's chess players (n=867) and 260 woman (n=32) and man (n=228) players were selected as a statistical sample by means of Morgan's chart and taking into account the percent of women and men in the community. The instrument for collecting information is included the Ottawa Mental Skills Assessment Tool (OMSAT-3) under three broader conceptual components: foundation, psychosomatic, and cognitive skills. The analysis of data were done by descriptive statistics and MANOVA (p<0.05). The results of study show that there are no significant difference in none of the areas of foundation, psychosomatic, and cognitive skills between men and women. Therefore, it seems that sex has no decisive role between man and woman chess players , mental skills with attention to there was no significant difference between man and woman's chess players in the other mental skills (p<0.05) and they were at the same level. Perhaps, we can conclude that both groups were likely to use the mental skills but there is no difference between the amounts of their use. So there is no significant difference between groups.

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 score0.012
Threshold uncertainty score0.206

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 studies0.0000.001
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 head0.020
GPT teacher head0.362
Teacher spread0.342 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sport Culture and ScienceSame topicSport Psychology and PerformanceFrench-language works237,207