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Record W2761943303 · doi:10.1080/07053436.2017.1378508

Two sides of time in the leisure experience of youth: Time investment and time perspectives

2017· article· en· W2761943303 on OpenAlexvenueno aff
Núria Codina, José Vicente Pestana

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsnot available
FundersEuropean Regional Development FundMinisterio de Economía y Competitividad
KeywordsLeisure timeFatalismTime perspectiveTime budgetPsychologySample (material)Investment (military)Adaptation (eye)Time allocationPerspective (graphical)Time perceptionSocial psychologyApplied psychologyPhysical activitySociologyComputer sciencePerceptionMedicineSocial sciencePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

The aim of this study is to discover which dimensions and aspects of the leisure experience are more closely related to time invested in leisure activities and to time perspectives (TPs). A sample of 231 young people aged between 18 and 24 years old responded to an adaptation of the Time Budget Technique and the Zimbardo Time Perspective Inventory. The results show that a leisure activity may be perceived as more freely carried out or more satisfactory without this experience correlating directly with the time invested in the activity. As far as the TPs are concerned, the present hedonistic correlates with the leisure experience, although there are cases (mass media use) where experience of the present is both hedonistic and fatalistic. Other leisure activities (hobbies and computing) produced a high level of satisfaction and were linked to the past positive and present hedonistic. The results indicate that the experience that accompanies certain leisure activities correlates with different TPs and time investments in these activities.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.405
Teacher spread0.337 · 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 designQualitative
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

Citations7
Published2017
Admission routes1
Has abstractyes

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