Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rewarding customers with own products or services has become an increasingly popular practice across a spectrum of industries such as airlines, hotels, and telecommunication. In these service industries, firms face demand uncertainty and strict short-term capacity constraint. When the market demand is low, firms hold excess capacities that would lead to intense price competition. In this paper we study the adoption and design of reward programs in the context of capacity management. We demonstrate that it is optimal for firms to offer capacity rewards when the market demand varies from one period to the other. By offering the reward programs, firms can effectively reduce available capacities when the market demand is low, and hence credibly show their unwillingness to undersell. Such a commitment can encourage their competitors to set their prices high. When firms provide reward programs, if a firm sets a higher price than the other and sells less today, in the future the firm can benefit from the other firm's larger reduction in available capacity through rewards. Thus, reward programs also provide additional incentives for firms to set higher current prices. Finally, since reward programs can add flexibility in adjusting the available capacities to the market demand, firms increase the size of regular capacities with reward programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it