Decision-making and coordination in the contract-farming supply chain with fairness concern and output uncertainty
Bibliographic record
Abstract
Supply chain members’ fairness concern, which may be disadvantageous or advantageous inequity averse, is a key factor affecting their cooperation and operation efficiency. Unfair distribution of profits will harm the efficiency of supply chain. In this paper, we consider a contract-farming supply chain consisting of an enterprise and a farmer with fairness concerns and uncertain output, where the enterprise is the leader and the farmer is the follower. We construct three Stackelberg game models: benchmark case without fairness concern, one with the enterprise’s inequity aversion and the other with both parties’. We study the impact of fairness concerns on the optimal decisions of supply chain members. The results show that: (1) When the farmer is neutral or faces disadvantageous inequity, the enterprise’s profit and expected utility are always smaller than that of the benchmark case, while the farmer is likely to benefit. (2) In contrast to previous studies, we find that an increase in wholesale price will lead to lower production input when both parties face disadvantageous inequity. (3) When both parties face advantageous inequity, it can achieve a win-win cooperation if the output uncertainty is high and the degree of the farmer’s advantageous inequity is low.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".