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Record W4408128664 · doi:10.5032/jae.v66i1.2394

Examining the Presence of Youth-Adult Partnership in Secondary Agricultural Educations: A Longitudinal Study

2025· article· en· W4408128664 on OpenAlexaff
Hunter Julian, Stacy K. Vincent, Kang Namkoong, Alex Preston Byrd, Brett Wasden, Ashley Austin, Morgan S. Dietrich

Bibliographic record

VenueJournal of Agricultural Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsAgricultural educationGeneral partnershipAgriculturePsychologyLongitudinal studySecondary educationDual enrollmentMathematics educationPedagogyBusinessGeographyMedicine

Abstract

fetched live from OpenAlex

The CROPS project began seven years ago with the intent of saving the lives of farmers and changing the behavioral safety intentions of teenage farm youth, through secondary agricultural education. Participating teachers from the ten-state region engaged in a three-day training that prepared them for content delivery through the tenets of the Youth-Adult Partnership (YAP) Theory. The purpose of the longitudinal qualitative study was to explore if the teachers a part of the CROPS project were engaging youth in the principles of YAP and to determine what elements of the theory of Authentic Decision-Making (Natural Mentors, Reciprocal Activity, and Community Connectedness) were present, through the interviews of randomly selected participating students (n = 263), over a six-year period, within 69 focus groups. After a thorough coding process, the students revealed 253 examples of YAP present. Natural Mentoring was determined to be the most prevalent and Authentic Decision-Making was deemed to be deficient. The interviews revealed growth in the presence of YAP over the six-year period, which is due to the modifications made in the training process; however, recommendations are provided to further elevate delinquencies and areas where YAP should be improved.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.349
Teacher spread0.293 · 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
Published2025
Admission routes1
Has abstractyes

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