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Record W2531209778 · doi:10.1123/jsep.2016-0061

Participant Perceptions of Character Concepts in a Physical Activity–Based Positive Youth Development Program

2016· article· en· W2531209778 on OpenAlexaff
Shaina Riciputi, Meghan H. McDonough, Sarah Ullrich‐French

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPositive Youth DevelopmentPsychologyIntrapersonal communicationCharacter developmentDevelopmental psychologySocial psychologyEthnic groupPerceptionThematic analysisMoral developmentCharacter educationCurriculumCharacter (mathematics)Applied psychologyPedagogyQualitative researchSociologyInterpersonal communication

Abstract

fetched live from OpenAlex

Physical activity-based positive youth development (PYD) programs often aim to foster character development. This study examined youth perspectives of character development curricula and the impact these activities have on their lives within and beyond the program. This case study examined youth from low-income families in a physical activity-based summer PYD program that integrated one character concept (respect, caring, responsibility, trust) in each of 4 weeks. Participants (N = 24) included a cross section of age, gender, ethnicity, and past program experience. Semi-structured interviews were analyzed using inductive thematic analysis and constant comparative methods. Thirteen themes were grouped in four categories: building highquality reciprocal relationships; intrapersonal improvement; moral reasoning and understanding; and rejection, resistance, and compliance. The findings provide participant-centered guidance for understanding youth personal and social development through physical activity in ways that are meaningful to participants, which is particularly needed for youth in low-income communities with limited youth programming.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.054
GPT teacher head0.397
Teacher spread0.343 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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