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Record W2923338397 · doi:10.5539/jel.v8n2p248

Sports Manager Training and Leadership Behaviors

2019· article· en· W2923338397 on OpenAlexvenueno aff
Elif Bozyiğit

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishPsychologyDimension (graph theory)Sample (material)Sport managementApplied psychologySocial psychologyData collectionMathematics educationManagementStatistics

Abstract

fetched live from OpenAlex

The aim of this study is to examine the leadership behaviors levels of university students studying in the Sports Management Department. The sample of the research consists of 148 students (male n=112, female n=36) aged between 18 and 28 years. In this study, the Personal Information Form was created in order to learn the characteristics of the participants such as gender, age groups, the status of doing sports as an athlete and the status of volunteering in sports events. The Leader Behavior Description Questionnaire (LBDQ), which was translated into the Turkish Language by Atar and Özbek (2009), was used as a data collection tool. The mean score of the students was 4.142 in the dimension of initiating structure and was 3.760 in the dimension of consideration. According to the results of the analysis, the initiating structure dimension scores differed according to variables of gender, the status of doing sports as an athlete and the status of volunteering in sports events. The consideration dimension scores differed age groups and the status of volunteering in sports events. In addition, it was found that there was a linear and significant relationship between scores of initiating structure and consideration.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.091
GPT teacher head0.394
Teacher spread0.303 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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