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Record W1545638904 · doi:10.18357/ijcyfs34.1201211557

EXPLORING DEVELOPMENTAL ASSETS IN UGANDAN YOUTH

2012· article· en· W1545638904 on OpenAlexvenueno aff
Christopher F. Drescher, Eu Gene Chin, Laura R. Johnson, Julie S. Johnson-Pynn

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
FundersMinisterio del Ambiente, Agua y Transición EcológicaMinistry of EnvironmentNational Geographic Society
KeywordsPositive Youth DevelopmentPovertyPsychologyPolitical scienceEconomic growthDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

Ugandan youth face a number of threats to their healthy development including poverty, high rates of disease, civil conflict, and environmental degradation. Cultivating developmental competencies is critical, not only for youth, but also for the future of Ugandan communities and civil society. In this article, we highlight contextual challenges facing Ugandan youth, report exploratory results on “standard” measures of developmental assets, and discuss the utility of a positive youth development (PYD) framework in Uganda. Despite difficult circumstances, our results indicated high levels of internal and external assets as assessed with the Developmental Assets Profile (DAP). The DAP demonstrated acceptable internal consistencies and was correlated with two other measures of youth assets, self-efficacy, and civic action. Although researchers should proceed with caution when using psychometric measures in new cultural contexts, our results provide preliminary support for the use of the DAP and a PYD framework for advancing adolescent research and programming in Uganda.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.217
GPT teacher head0.351
Teacher spread0.134 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2012
Admission routes1
Has abstractyes

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