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Record W4251649903 · doi:10.32920/ryerson.14645496

Measuring the effectiveness of educational instruments in facilitating environmentally responsible behaviour in agriculture : the Canada-Ontario Environmental Farm Program

2021· preprint· en· W4251649903 on OpenAlexaboutno aff
Hayley Vernon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPlan (archaeology)AgricultureEducational programEnvironmental educationPsychologyTheory of planned behaviorStrengths and weaknessesBusinessApplied psychologyEnvironmental resource managementPedagogyPolitical scienceSocial psychologyGeographyManagementEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

This research evaluates the effectiveness of Ontario's voluntary Environmental Farm Plan (EFP) program's educational instruments by applying the Theory of Planned Behaviour and measuring environmental awareness. Despite being billed as an educational and environmental awareness program, the educational elements of the EFP program have not previously been evaluated in relation to their ability to foster environmental behaviours. A survey was conducted before and after the EFP workshops to measure any change in farmers' environmental awareness or intentions to implement beneficial management practices. Findings suggest an overall increase in participants' behavioural intentions and awareness, though there are opportunities to strengthen the underlying constructs of these measures to help ensure these positive outcomes are sustained beyond workshop participation. This research identifies the strengths and weaknesses of the EFP program's educational instruments and provides insight into the psychological constructs influencing farmers' participation in the program and their intentions to implement conservation measures.

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)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.141
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 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
Published2021
Admission routes1
Has abstractyes

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