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Adolescent Girls’ Leisure: A Conceptual Framework Highlighting Factors That Can Affect Girls’ Recreational Choices

2002· article· en· W2091351458 on OpenAlexaboutno aff
Kandy James, Lynn Embrey

Bibliographic record

VenueAnnals of Leisure Research · 2002
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
FundersHealthway
KeywordsRecreationVignetteAffect (linguistics)Conceptual frameworkCompetence (human resources)PsychologyPhysical activitySocial psychologyPublic relationsSociologyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Abstract In Australia adolescent girls are less fit than boys, and this is a matter of some concern. This has also been found in American and Canadian studies. Although many adolescent girls engage in physically active recreation activities in public places, others choose more passive recreation in private spaces, which has implications for their health. This article shows how a conceptual framework can be useful to provide a pictorial representation of the relationships between factors that can affect girls’ leisure choices and their potential outcomes. For example, prior to choosing to participate in a recreational activity, girls often weigh up the potential for ridicule of their physical appearance or athletic competence against the potential enjoyment of the activity. This paper contains a vignette to illustrate how the framework can work, followed by potential strategies to consolidate or challenge the framework, and how it could be used to develop strategies for change. This snapshot of girls’ decision-making processes should be of use to recreation programmers, facility providers and education authorities. It can help to focus efforts to improve factors that increase girls’ levels of participation in healthy physical activity and decrease those factors that inhibit 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.392
GPT teacher head0.460
Teacher spread0.068 · 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.

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

Citations10
Published2002
Admission routes1
Has abstractyes

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