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Record W4214745506 · doi:10.5114/hm.2022.108323

Describing the tactical knowledge used by young competitive soccer players: A psychophenomenological analysis

2022· article· en· W4214745506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Movement · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMovement (music)SociologyPsychologyPhysics

Abstract

fetched live from OpenAlex

Purpose Decision-making is the process through which players choose the most appropriate action to perform in the play. Previous investigations did not clearly portray the specific decisional background of learning players considering the progressing state of their capabilities and game knowledge. The study aimed to describe significant information picked up in situ and how young soccer players applied it to make decisions in the play. Methods Three male soccer players aged 14 years were interviewed after 2 official district championship games in Portugal. Their games were filmed; the video sequences showing offensive actions were extracted and edited for visualization. Before questioning, each sequence was visualized for recalling the game actions. The explicitation interview technique was used to help the athletes describe in detail their recalled actions. In line with the recommendation in similar studies, a content analysis of the interviews was conducted to identify the decisional background and the links between elements of information picked up in situ and the decision itself. Results The players did not perform a detailed judgement for every decision and were influenced by direct constraints such as opponent pressure. In contrast, they occasionally assessed risks and opportunities emerging in the game depending on their colleagues’ actions and the pitch zone. At times, they relied on their imagination of what their teammates would do with the action outcome. Conclusions Key elements of the decisional background are common among learning players and can be used as a reference for further investigation or practical intervention in game teaching.

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.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0600.000

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 head0.257
GPT teacher head0.480
Teacher spread0.224 · 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