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Record W2947482435 · doi:10.1123/iscj.2019-0044

Sport-Specific Free Play Youth Football/Soccer Program Recommendations Around the World

2019· article· en· W2947482435 on OpenAlexaboutno aff
Marty K. Baker, Jeffrey A. Graham, Allison Smith, Zachary T. Smith

Bibliographic record

VenueInternational Sport Coaching Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsFootballFootball playersAdvertisingPsychologyPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

The purpose of this Coaching In paper is to share an overview of how sport-specific free play is incorporated into training and development recommendations for youth football (soccer) in various countries around the world. A review of 11 countries’ training programs was conducted, in which specific instances of training recommendations were examined to identify similarities and differences among nations. Results of our review suggest that not all of the programs emphasized children having fun, enjoying the game of football, or engaging in free play. For example, the program from England strongly emphasized outcome related abilities more than enjoyment or play related features of training. In contrast, the Italian, Canadian, and Australian documents discussed that allowing youth to play freely engaged children, ensured they were having fun, and encouraged a fascination with football. Programs recommending developmental games or free play often suggested the use of purposeful gameplay that resembled traditional competition or match-specific situations. Examining development recommendations across nations provides important insight into how youth sport development efforts are shaped around the world, especially as youth sport coaches seek to enhance youth engagement, while simultaneously helping youth improve their skills.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.099
GPT teacher head0.458
Teacher spread0.359 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2019
Admission routes1
Has abstractyes

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