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Record W3138506530 · doi:10.5539/jel.v10n2p152

An Investigation on Self-Handicapping Levels of Sport Management Students

2021· article· en· W3138506530 on OpenAlexvenueno aff
Ahmet Yalçınkaya, Ziya Bahadır, Çağrı Hamdi ERDOĞAN

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishPsychologyLikert scaleMedical educationMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

This study aims to determine the self-handicapping levels of sports management department students. Furthermore, the study endeavors analyzing the sports management students’ self-handicapping level by gender, grade, grade point average, and exercise status. The study group analyzed with the survey method comprise 158 students enrolled in the Faculty of Sports Science for Sport Management Department at Erciyes University, Kayseri/Turkey during the 2018-2019 academic year. The study utilizes “Self-Handicapping Scale” developed by Jones and Rhodewalt (1982) and adapted to Turkish by Akın, Abacı and Akın (2010) as the data collection tool. The SPSS program was used for data analysis. The results indicate that the sports management students’ self-handicapping level in the study group was “moderate”. Moreover, the self-handicapping levels of the sports management students in the study group did not differ significantly by gender, grade, age and exercise status (p > 0.05). However, it was discovered that self-handicapping levels significantly differ by grade point average (p < 0.05).

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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