To be or to suffer? The role of cognitive dissonances in the cognitive locking of farm structural investment: an interdisciplinary perspective
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".