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The Change towards the Integration of Agri-environmental Practices (CIAEP) into farmer’s practices system: An affective, cognitive, and behavioural process

2024· article· en· W4404405640 on OpenAlexaffabout
Aurélie Dumont, Julie Ruiz, Stéphane Campeau

Bibliographic record

VenueJournal of Rural Studies · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsProcess (computing)CognitionPsychologyBusinessComputer science

Abstract

fetched live from OpenAlex

This article contributes to the research characterising agri-environmental practices (AEPs) adoption processes. Although the adoption of AEPs by farmers has long been recognised as a process, existing conceptualisations vary significantly and face theoretical and operational limitations that limit the ability of research to accurately describe and understand these processes. In this paper, we propose a new conceptualisation of AEPs adoption processes, supported by psychological theories and data from a longitudinal case study of 20 farmers in Quebec, Canada. We develop an empirically informed operational framework for analysing AEP adoption processes: the Change towards the Integration of Agri-environmental Practices (CIAEP) framework. The CIAEP focuses on the internal processes farmers undergo to integrate AEPs, framing integration as a process involving cognitive, affective, and behavioural changes, including the acceptance of the loss of previous practices. It defines a seven-stage process with associated indicators, ranging from no intention to change to the integration of an AEP into a farming practices system. By including affective dimensions alongside established cognitive and behavioural components, the CIAEP enriches existing conceptualisations of AEP adoption processes. It introduces new stages that explain how farmers decide to commit to adopting AEPs and defines integration as the technical, affective, and cognitive mastery of an AEP, offering a fresh perspective on the concept of adoption. This article outlines the development of the CIAEP framework, its application in the case study, and provides two examples of its use based on partial longitudinal data. Finally, we discuss the benefits, limitations, and research opportunities presented by the CIAEP framework. • This research presents the ‘Change towards Integration of Agri-environmental Practices' (CIAEP) framework. • An operational analysis framework to study the adoption process. • 7 stages described by behavioural, cognitive and affective indicators. • A validation with data from a six-year longitudinal study of 20 farmers.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.572

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 studies0.0010.000
Scholarly communication0.0000.002
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 head0.163
GPT teacher head0.373
Teacher spread0.210 · 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 designOther design
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

Citations4
Published2024
Admission routes2
Has abstractyes

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