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Record W3126389815 · doi:10.1111/fcsr.12390

Determining Human Development Competency Training Needs of Family and Consumer Sciences Extension Professionals

2021· article· en· W3126389815 on OpenAlexaff
Alisha M. Hardman, Marina Denny, AlliGrace Story, Geoffrey C. Denny

Bibliographic record

VenueFamily and Consumer Sciences Research Journal · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsSun Gro Horticulture (Canada)
Fundersnot available
KeywordsWarrantExtension (predicate logic)PsychologyService (business)Medical educationRank (graph theory)Training (meteorology)Family and consumer scienceMarketingKnowledge managementBusinessMedicineComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Cooperative Extension Service (also known as Extension) agents may lack adequate content knowledge to meet the demands of the diverse programming in Family and Consumer Sciences (FCS) they are expected to deliver. This article reports on the findings from an assessment of FCS Extension personnel in Mississippi. Two surveys were administered to assess the perceived importance of FCS competencies and the perceived ability to perform them. A rank‐sum score was computed, which was used to determine the relative need for training on various Human Development competencies. Findings show that competencies related to child development, parenting, and family stress and crisis warrant training.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0050.003
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 head0.362
GPT teacher head0.422
Teacher spread0.059 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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