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Record W4416186422 · doi:10.1007/s41130-025-00244-1

To be or to suffer? The role of cognitive dissonances in the cognitive locking of farm structural investment: an interdisciplinary perspective

2025· article· en· W4416186422 on OpenAlexaffabout
Mikaël Akimowicz, Sophie Thiron, Denis Réquier-Desjardins, Karen Landman, Charilaos Képhaliacos

Bibliographic record

VenueReview of Agricultural Food and Environmental Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Guelph
FundersResearch Executive AgencyFP7 People: Marie-Curie ActionsEuropean Commission
KeywordsPerspective (graphical)CognitionInvestment (military)Cognitive reframingSocio-cognitiveCognitive style

Abstract

fetched live from OpenAlex

Abstract While concerns about farmers’ mental health have risen all over the world over the last twenty years, the impact of farm structure on farmer’s ill-being has been discussed only recently. In this article, farm investment decision-making is scrutinized in an attempt to shed light on the connection between farm structure and current farmers’ ill-being with an interdisciplinary conceptual framework. The institutionalist approach aims to integrate the anthropological concept of sacred and the psychological concept of cognitive dissonances. The interviews of 41 urban-influenced farmers in/around the Ontario’s Greenbelt, ON, Canada, and in Toulouse InterSCoT rely on the design of their investment decision-making mental models during semi-structured interviews. Results corroborate that the internalization of the norms of the current dominant agricultural model has solved cognitive dissonances and contributed to the adoption of the modern farmer identity. Nowadays, cognitive dissonances appear to be triggered by the diverging societal demands faced by 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.496
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.294
Teacher spread0.274 · 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 routes2
Has abstractyes

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