The Attribution of Self Amongst Australian Family Farm Operators: Personal Responsibility and Control
Bibliographic record
Abstract
Australian agriculture is dominated both numerically and in terms of production by family owned and operated farm enterprises. Many family farmers struggle to maintain farm viability amidst the ongoing commitment to a trade liberal paradigm in Australian agricultural policy. Significantly, governmental neoliberal discourses insist on Australian farmers taking personal responsibility and control for any socio-economic hardship or farm viability problems they face and down play structural explanations. In this article we argue that the intent of this discourse, if internalised by individual family farm operators, creates the potential for self-blame where fanners “fail”. To investigate this argument, open-ended responses from a survey of farmers in a NSW rural local government area were examined using an extension of attribution theory from social psychology. The analysis identifies how individual family farm operators have actually engaged with these discourses and the extent to which the attributions these discourses encapsulate are replicated, transformed, or contested. Areas for future research, including impacts of attributions on family farm operators’ psychological health, are discussed.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".