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Record W758343187

A Study on the Rural Youth Programs of Foreign Countries

2001· article· en· W758343187 on OpenAlexaboutno aff
Jeong-Joo Kim, Hae-Sub Oh

Bibliographic record

VenueThe Journal of Agricultural Extension · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPositive Youth DevelopmentEconomic growthPolitical scienceRural areaRural managementAgricultureRural developmentGeography
DOInot available

Abstract

fetched live from OpenAlex

The purposes of this study were to review the rural youth programs of selected foreign countries and to draw some implications to the rural youth programs in Korea. The youth development programs reviewed were 4-H Youth Development Program of U.S.A, Rural Youth Information Service of Australia, Rural Youth Job Strategy of Canada, The Urban-Rural Youth Program of U.S.A, and Expert Consultation on Extension Rural Youth Programmes and Sustainable Development of FAO. After reviewing the rural youth programs of selected foreign countries, the authors suggested the following implications for further development of rural youth programs in Korea; 1. The target group of rural youth programs should include urban youth as well as rural youth, and the programs should be focused on enlarging their awareness of the rural community and agriculture. 2. Rural youth programs should be extended beyond agricultural sectors, such as leadership, career development, leisure activity, and cultural life. 3. We should develop some programs to support academic achievement, career development, employment, cultural needs including some strategies to prevent problematic behaviors of rural youth. 4. Rural youth should be supported more opportunities to join the community life to experience and learn various life skills through active participation such as interpersonal skill, leadership skill, and problem-solving skill.

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 score0.246
Threshold uncertainty score0.232

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 head0.029
GPT teacher head0.226
Teacher spread0.197 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

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