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Record W3086011377 · doi:10.1016/j.jsams.2020.09.005

Similarities and differences between sports subserving systematic talent transfer and development: The case of paddle sports

2020· article· en· W3086011377 on OpenAlexfundno aff
Jan Willem Teunissen, Stijn ter Welle, Sebastiaan Platvoet, Irene R. Faber, Johan Pion, Matthieu Lenoir

Bibliographic record

VenueJournal of science and medicine in sport · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersCanadian Academy of Sport and Exercise Medicine
KeywordsSprintJumpingTrampolineThrowingPaddleClimbingApplied psychologyPsychologyAnthropometryPhysical medicine and rehabilitationPhysical therapyEngineeringMedicineAeronautics

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to investigate similarities and differences for 18 sports toward canoe/kayak in order to identify donorsport and/or multisports, based upon a systematic analysis of the task constraints per sport that are assumed to be either crucial or less important from the coaches' viewpoint. DESIGN: Descriptive survey analysis. METHODS: 891 certified coaches from 19 sports valued (0-10; not important at all-very important) 15 characteristics by a questionnaire (Flemish Sports Compass) within their sport. Unique sport-profiles (discriminant analysis - DA) were constructed for 19 sports based on these characteristics. Similarities and differences between canoe/kayak and the other 18 sports were analyzed by means of MANOVAs on anthropometric, physical and motor coordination characteristics. RESULTS: Cross validated DA (rcan=0.660, Wilks' Lambda=0.564, p<0.001) showed that 72.1% of the canoe/kayak coaches were correctly assigned to their sport. For canoe/kayak seven characteristics were valued crucial; dynamic balance (8.51±1.69), core stability (8.45±2.27), pulling power (8.12±1.68), speed (7.54±2.07), endurance (7.27±2.03), stature (6.43±1.41) and rhythm (6.01±3.01). Least important characteristics were: flexibility (6.16±1.75), agility (4.27±3.10), catching (3.90±3.22), climbing (2.45±3.05), jumping (1.81±2.11), throwing (1.60±2.24), hitting (.94±1.77) and kicking (.61±1.04). CONCLUSIONS: This novel approach to determine important characteristics per sport makes identifying similarities and differences between sports possible. Similarities might enlarge talent-pools for possible talent transfers. Differences can help identify sports based on complementary characteristics for the construction of broad motor development programs. From this viewpoint gymnastics can serve as potential donorsport (similarities) for canoe/kayak, while handball and tennis can subserve broad development for young canoe/kayak athletes.

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)0.002
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 score0.027
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.286
Teacher spread0.234 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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