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Record W2884578806 · doi:10.5430/wje.v8n4p1

Comparison of Academic Suspension Levels of Faculty of Sports Sciences Students and Faculty of Education Students

2018· article· en· W2884578806 on OpenAlexvenueno aff
Latif Aydos, Haluk Koç, Hacı Ahmet Pekel

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Physical educationLikert scalePearson product-moment correlation coefficientSignificant differenceMathematics educationMedical educationMedicineMathematicsStatisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

Faculty of Education students in terms of age, gender, active sports making, department, graduated high school,mother education level, father education level, mother profession and father occupation variables have beenexamined. A total of 364 university students studying in the Faculty of Sport Sciences and the Faculty of Educationin Gazi University, Hacettepe University and Ankara University were formed the Research group in the academicyears 2017-2018. The Academic Suspension Scale developed by Çakıcı in 2003 was used in the study. It wascompared average scores in unrelated measures, Variance Analysis (one-way ANOVA, independent sample t-test) forcomparison of mean scores in unrelated measures, the Tukey test to determine which groups had significantdifferences were used. In addition, a Pearson Correlation analysis was conducted to test the relationship between theparticles collected in the single sub-dimension. Findings obtained from this research reveal that the students ofFaculty of Sports Sciences showed more academic suspension behaviors than the students of Faculty of Education.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.118
GPT teacher head0.505
Teacher spread0.387 · 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
Published2018
Admission routes1
Has abstractyes

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