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Record W2941415699 · doi:10.5539/hes.v9n2p131

Internet Addiction: Relationship with Perceived Freedom in Leisure, Perception of Boredom and Sensation Seeking

2019· article· en· W2941415699 on OpenAlexvenueno aff
Feyza Meryem Kara

Bibliographic record

VenueHigher Education Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsBoredomSensation seekingPsychologySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

This study aimed to examine the university student’s internet addiction, perceived freedom in leisure, leisure boredom, and sensation seeking level with regard to gender and physical activity participation, and to investigate the relationship between internet addiction, perceived freedom in leisure, leisure boredom and sensation seeking. The participants who were chosen using a convenience sampling method filled the “Short Form of Young’s Internet Addiction Test” (YIAT-SF), Perceived Freedom in Leisure Scale (PFLS), “Leisure Boredom Scale (LBS), and “Sensation Seeking Scale” (SSS). T-test, MANOVA, ANOVA and correlation analysis were used to analyze the data. T-test results indicated there were no significant differences in the mean scores of “YIAT-SF” with respect to gender (p> 0.05). However, analysis revealed significant differences in the mean scores of “YIAT-SF” with regard to not regularly physical activity participation. There were significant differences in the mean scores of “PFLS” in favor of men participants and regularly physical activity participants (p<0.05). Gender and regularly physical activity participation were significant of "LBS” (p<0.01) in favor of women participants (p<0.05). Similarly, gender were significant of “SSS” (p<0.01) on the all sub-dimensions in favor of men participants (p<0.05). However, there were no significant differences in the mean scores of regularly physical activity participation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.788

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)0.0010.000

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 head0.048
GPT teacher head0.332
Teacher spread0.284 · 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 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

Citations23
Published2019
Admission routes1
Has abstractyes

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