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Record W1635217239 · doi:10.1111/labr.12049

Youth Training Programs and Their Impact on Career and Spell Duration of Professional Soccer Players

2015· article· en· W1635217239 on OpenAlexaff
Mihailo Radoman, Marcel Voia

Bibliographic record

VenueLabour · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpellLeagueDuration (music)EndogeneityReputationPsychologyCovariateDemographic economicsSet (abstract data type)Instrumental variableMarketingEconometricsEconomicsPolitical scienceBusinessSociologyComputer science

Abstract

fetched live from OpenAlex

Abstract A unique data set of post‐war English trained soccer players is used to study the impact of the youth training program they attended on their career and spell duration. Duration models in the spirit of Abbring and van den Berg are employed to estimate local treatment effects of different training programs on players — survival in the top European leagues. The results indicate that the duration patterns of players are dependent on the youth academy they attended. Certain clubs, with a well‐established reputation in developing youth talent, outperform others in terms of producing and evaluating the ability of their youth players to succeed in top European leagues. The spell analysis outlines the nature of the competitive environment in which smaller clubs have a chance to keep up with the larger ones in terms of producing and holding on to homegrown talent. Finally, the results of both analyses addressed unobserved heterogeneity, allowed for nonlinearity of covariates using the cubic spline methodology, and were tested for endogeneity bias using a split sample test.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.105
GPT teacher head0.254
Teacher spread0.149 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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