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Record W3005664166 · doi:10.1371/journal.pone.0228611

Relative age effects in Elite Chinese soccer players: Implications of the ‘one-child’ policy

2020· article· en· W3005664166 on OpenAlexaff
Zhen Li, Lijuan Mao, Christina Steingröver, Nick Wattie, Joseph Baker, Jörg Schorer, Werner Helsen

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsYork UniversityOntario Tech University
FundersScience and Technology Commission of Shanghai Municipality
KeywordsDemographyChinaEliteAge groupsPsychologyGeographySociologyPolitical science

Abstract

fetched live from OpenAlex

The relative age effect (RAE) refers to the asymmetrical distribution of birthdates in a cohort found in many achievement domains, particularly in sports with many participants like soccer. Given the uniqueness of the one-child policy in China, this study examined the existence of the RAE in elite Chinese male and female soccer players generally and relative to their playing position on the field. Results showed a clear and obvious RAE for all age groups (U20 male, U18 male, adult female and U18 female) with the observed birthdate distributions for each age group significantly different from expected distributions (p<0.05). Additionally, we noticed a differential RAE according to the players' position on the field as reflected in different effect sizes. In male players, the RAE was significantly greater in Defenders (DF) and Goalkeepers (GK) compared to Midfielders (MF) and Forwards (FW) (VDF = 0.266>VGK = 0.215>VMF = 0.178>VFW = 0.175). In female players, GKs had a larger RAE (VGK = 0.184>0.17, VDF = 0.143, VMF = 0.127, VFW = 0.116). To reduce the negative consequences associated with RAEs throughout player development systems, potential solutions are discussed.

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)0.000
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 score0.024
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

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

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 head0.043
GPT teacher head0.286
Teacher spread0.243 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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