Incentive Mechanism Design in Retailer Private-label Business under Random Yield: A Principal-agent Model with Hidden Actions
Bibliographic record
Abstract
cal centre of Palermo, not only for its history but also for its architectural features.One discovers its unique position seeing the exclusive view from the panoramic terrace on the bay.Over the course of time the palace has welcomed Kings, Princes, Presidents and many famous personalities, and still today enjoys an active life hosting exhibits and important events.Delegates will enjoy the spacious and opulent salons of the Palazzo Butera that make it the perfect location for any kind of event.As Conference chair and vice-chair, we have been assisted by many able colleagues and we thank all of the individuals and organizations that have made this conference possible.They include the EurOMA Board and its Event and Meetings Team, the Scientific Committee, our Keynote Speakers, the contributors to the Special Sessions, the Session Chairs, the Review Committee for the Chris Voss and Harry Boer Best Paper Awards and the Local Organising Committee.We thank particularly the teams at the C&S Congressi and at EIASM with whom we have worked closely and so well over the past two years.Finally, we acknowledge gratefully the financial support of our sponsors and advertisers and the support of the University of Palermo.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".