Research on contract selection for collaborative innovation in China's photovoltaic industry supply chain from the perspective of supply-demand imbalance
Bibliographic record
Abstract
Based on the perspective of supply-demand imbalance, this article constructs a collaborative innovation decision-making model for the photovoltaic industry supply chain using differential game theory. It analyzes two different decision-making scenarios: decentralized decision-making and centralized decision-making, and focuses on studying the impact of cost sharing contracts under decentralized decision-making and revenue sharing contracts under centralized decision-making on the decision-making behavior of photovoltaic industry supply chain entities. Research has found that: 1) The supply-demand imbalance coefficient affects the pricing of photovoltaic silicon wafer suppliers. 2) In the case of decentralized decision-making, considering the cost sharing contract situation in the photovoltaic industry supply chain system can better optimize the profit value and innovation level of each subject in the photovoltaic industry supply chain. 3) The benefit sharing contract under centralized decision-making is more effective than the cost sharing contract under decentralized decision-making.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".