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Record W7056052976

The development of leadership in model youth football coaches

2017· article· en· W7056052976 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFootballLeadership developmentCoachingLeadership studiesLeadership styleLeadershipShared leadership
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the development of leadership among model youth football coaches. Six award-winning model youth football coaches (M age = 46.0 years, SD = 5.8), and one athlete from each stage of the coaches' careers (early, middle, recent; n = 18, M age = 24.4 years, SD = 4.3) were purposefully sampled and completed semi-structured interviews. Interviews included questions on leadership behaviours and factors that have influenced the development of the coaches' leadership. Deductive and inductive analyses were completed. First, data regarding the coaches' leadership behaviours from the coaches' and athletes' interviews were deductively coded into categories from the charismatic, ideological, and pragmatic (CIP) model of outstanding leadership (Mumford, 2006). The majority of reported behaviours the coaches used aligned with a pragmatic leadership style. However, none of the coaches' behaviours exclusively aligned with a single style. Second, data from the coaches' interviews were inductively analysed to identify factors that contributed to the development of their leadership. The following factors were identified: role models; networks of coaches; experience and reflection; and formal, non-formal, and informal learning. These factors were consistent, regardless of the coaches' leadership styles. Overall, the results of this study indicated that there may be benefit to considering broader models of leadership in coach education and in the study of leadership in sport, and establishing alternative coach education pathways for leadership development.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.185
GPT teacher head0.303
Teacher spread0.119 · 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 designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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