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Record W2612368930

Using positive youth development through sport to promote the United Nations Millenium Development goals

2010· article· en· W2612368930 on OpenAlexaff
William R. Falcão, Gordon A. Bloom, Wade Gilbert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsMcGill University
Fundersnot available
KeywordsMillennium Development GoalsSummitPositive Youth DevelopmentRecreationPolitical sciencePsychologyPublic relationsEconomic growthPovertyDevelopmental psychologyGeography
DOInot available

Abstract

fetched live from OpenAlex

In 2000, the United Nations (UN) hosted a world summit to set their major goals and objectives. This summit produced the UN Millennium Development Goals (MDGs), a set of eight quantified goals that addressed key social problems in the world. The UN has recognized the potential of sport and physical activity settings for addressing the MDGs. The coach plays the primary role in shaping the youth sport environment, and consequently has a major impact on the quality of youths' experience in sport. Framed around the principles of positive youth development, this study developed and implemented sport-related activities which addressed the UN MDGs of health, education, and empowering women. Participants included six youth sport coaches from both recreational and competitive leagues. Multiple methods were used to collect data. Coaches perceived the project as successful and the activities were seen as beneficial for athletes and for the team. In particular, the coaches believed the activities improved the athletes' values towards health, education, and empowering women. In addition, coaches believed the activities increased team cohesion and their players showed more caring, compassion, and character (indicators of PYD). Overall, results demonstrated that typical youth sport settings can be used to teach citizenship skills and promote PYD.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.000
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 head0.076
GPT teacher head0.345
Teacher spread0.269 · 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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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