Bargaining power and risk from substitutability between products attributes the case of specialty eggs in Canada
Bibliographic record
Abstract
Abstract Supply managementpolicy covers conventional egg production in Canada and uses production costs to determine producer prices. The context differs for specialty eggs since graders and farmers individually negotiate the price premiums. Because specialty egg production, such as cage‐free or organic production, involves important fixed‐cost farm investment, it is of interest to assess potential bargaining power in the value chain, especially given the significant commitments from Canadian retail stores and fast food restaurants to move exclusively to cage‐free eggs in the coming years. This article develops a theoretical model of joint profit maximization and price adjustment under risk. Due to data availability, a reduced version of the proposed model is used to empirically test the bargaining power along the value chain for specialty eggs. Although the estimations concentrate on the bargaining power of producers, other actors in the value chain are considered. The results show that the bargaining power of downstream actors is greater than that of producers in most provinces and for most specialty eggs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".