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Record W4377220974 · doi:10.3389/fspor.2023.1046937

How sport changed my life? Description of the perceived effects of the experiences of young Colombians throughout a sport for development and peace program

2023· article· en· W4377220974 on OpenAlexaff
Tegwen Gadais, Natalia Varela, Victoria Eugenia Soto, Sandra Vinazco, Mauricio Garzón

Bibliographic record

VenueFrontiers in Sports and Active Living · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsClubAthletesPerceptionPolitical scienceLatin AmericansPublic relationsPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Introduction: This study contributes to the advancement of the field of Sport for Development and Peace (SDP) research in Latin America and the Caribbean (LAC). There are still few studies on SDP programs in this region and it is important to document and understand the impacts of these programs on participants. Methods: The present study is the result of a collaborative research that aims to describe the experiences and perceptions of Colombian youth and program managers who participated in an SDP program that took them from a local community sports club to the Olympic Games. Seven semi-structured interviews were conducted with key actors (administrators, coaches, and athletes) who participated in a triple and transversal (local, district and national) Olympic walking training program. Results: The results provided a better understanding of the program dynamics in the local, regional, and national level, as well as of the short- and long-term effects perceived by the actors of the process on their development, education, health, and career. Recommendations are made for SDP organizations in LAC. Discussion: Future studies should continue to investigate the SDP initiative in LAC to understand how sport can help development and peace building in this region.

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 score0.189
Threshold uncertainty score0.308

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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