Sociological Factors Affecting Agricultural Price Risk Management in Australia*
Bibliographic record
Abstract
Abstract The highly volatile auction system in Australia accounts for 85 percent of ex‐farm wool sales, with the remainder sold by forward contract, futures, and other hedging methods. In this article, against the background of an extensive literature on price risk strategies, we investigate title behavioral factors associated with producers' adoption of price risk‐management strategies (specifically futures and forward contracts) for selling wool. This research presents a behavioral model based on Diffusion of Innovations, the Theory of Reasoned Action, and the Theory of Planned Behavior. We found that the auction system is used as a price risk‐management tool because other selling methods are considered more risky. We also report on a curious relationship between risk and complexity in terms of wool producers' intentions to use forward contracts. We explored sociological factors in conjunction with focus‐group data in an attempt to understand this relationship. This exercise yielded some interesting findings on the impact that trust, habit, social cohesion, and networks have on decision making in the rural community. The significance of this article lies in its application of core sociological theory in a new research context: the Australian wool industry.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".