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Record W2045056034 · doi:10.2466/23.25.pms.118k12w5

From Specific Training to Global Shift of Manual Preference in Kung Fu Experts

2014· article· en· W2045056034 on OpenAlexaff
Rodrigo S. Maeda, Rosana Machado de Souza, Luís Augusto Teixeira

Bibliographic record

VenuePerceptual and Motor Skills · 2014
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPreferenceHand preferencePsychologyPerspective (graphical)Cognitive psychologyAthletesLateralityFood preferenceDevelopmental psychologyComputer sciencePhysical therapyArtificial intelligenceStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

Manual preference and intermanual performance asymmetry have been approached from a multidimensional and dynamic perspective. A point of interest from that approach is the role of lateralized motor experiences on handedness. In this study, intermanual performance asymmetry in sport-specific movements and manual preference in daily living tasks were compared between Kung Fu athletes and novices. Analysis of movement time in the performance of interlaterally symmetric and asymmetric movement patterns showed smaller intermanual performance asymmetry in experts. Analysis of manual preference using the Edinburgh Handedness Inventory indicated that experts presented predominantly weak or moderate strength of right hand preference. Novices, conversely, were found to have predominantly strong right hand preference. These results suggest that extensive bimanual training by experts leads to a global shift of manual preference, affecting hand selection in distinct tasks.

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 score0.002
Threshold uncertainty score0.007

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 science0.0000.001
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 head0.047
GPT teacher head0.287
Teacher spread0.240 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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