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
This paper examines a key difference between two promotional vehicles, coupons and rebates. Whereas coupons offer deals up front, with the purchase of the product, rebates can be redeemed only after purchase. When consumers experience uncertain redemption costs, this difference translates to a difference in when uncertainty is resolved. With coupons the uncertainty is resolved before purchase; with rebates the uncertainty is resolved after purchase. As a result, we show that rebates are more efficient in surplus extraction but coupons offer more finetuned control over whom to serve. We identify the conditions under which each is optimal, and these conditions turn on the gap between “low” reservation price consumers’ valuations and their highest redemption costs. Rebates are optimal when this gap is large; coupons tend to be optimal otherwise. Risk aversity on the part of consumers reduces the attractiveness of rebates, as does the delay between rebate redemption and rebate payment, but the latter if and only if consumers are more impatient than the seller. These observations match up well with what we know about the use of these promotional vehicles in the real world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it