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Record W4281770010 · doi:10.5539/jas.v14n7p36

Vocational Training of Young Farmers: The Case of Students of the American Farm School Vocational Training Institute

2022· article· en· W4281770010 on OpenAlexvenueno aff
Anna Papakonstantinou, Marios Koutsoukos, Konstantinos Zoukidis, Evangelos Vergos

Bibliographic record

VenueJournal of Agricultural Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationExperiential learningAgribusinessTraining (meteorology)Investment (military)Likert scaleSustainabilityMarketingPsychologyBusinessKnowledge managementPedagogyMedical educationPolitical scienceAgricultureComputer scienceGeography

Abstract

fetched live from OpenAlex

The purpose of this research is to investigate the views of young farmers regarding individual dimensions of vocational training as applied to students of the Vocational Training Institute (VTI) of the American Farm School. Processed data arriving from specially formulated Likert-level questions revealed that young farmer students find it absolutely necessary to participate in training programs governed by experiential learning methodologies, while recognizing that these processes are linked to their knowledge and skill improvement for developing a firm future professional consignment in the agrifood sector. To that extent, experiential learning applications would subsequently assist program participants to cultivate and grow a better understanding for innovation, which undoubtedly affect the path of agribusiness investment sustainability. Although current national sectoral progress has taken significant steps ahead, still not being enough satisfactory in terms of competitiveness. However, there has to be always tension to gradually organize and execute professional experiential learning methodologies from specialized educational organizations to concretely reach out higher levels of improvement in rural development alongside with other eminent and specialized important factors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.306
Teacher spread0.242 · 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
Published2022
Admission routes1
Has abstractyes

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