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Record W2886536170 · doi:10.34068/joe.56.02.24

Ramping Up Rural Workforce Development: An Extension-Centered Model

2018· article· en· W2886536170 on OpenAlexaff
Carolyn J. Hatch, Cheryl Kriesel, Kenneth Sherin

Bibliographic record

VenueJournal of Extension · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkforceExtension (predicate logic)Citizen journalismProcess (computing)BusinessWorkforce developmentWorkforce planningKey (lock)Economic growthRural developmentProcess managementPublic relationsKnowledge managementPolitical scienceEconomicsGeographyComputer scienceAgriculture

Abstract

fetched live from OpenAlex

Workforce development is a growing need in rural communities. This article recognizes Cooperative Extension as a critical labor market intermediary in fostering local workforce solutions. It proposes a community-based approach with Extension at the center of a process for identifying key stakeholders, facilitating collaboration, and supporting data-driven decisions. Through participatory methods and economic analysis of local industries, our team engaged over 120 stakeholders from two rural regions in the Great Plains. Our findings show that Extension plays an important role in promoting cross-sectoral collaboration to address complex workforce issues, enhance community capacity, and mobilize local action.

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)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0070.006
Open science0.0030.009
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.131
GPT teacher head0.312
Teacher spread0.181 · 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 designTheoretical or conceptual
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

Citations3
Published2018
Admission routes1
Has abstractyes

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