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
Simple selling mechanisms are very appealing in practice, yet they may limit the firm’s ability to extract a large consumer surplus. In this paper, we study the efficiency of a simple pure bundling mechanism in extracting the consumer surplus in the presence of nonnegative marginal costs and correlated valuations. The main question we address is how to quantify the benefits of adopting a simple pure bundling mechanism relative to other more complicated mechanisms, such as mixed bundling. We develop simple robust analytics that identify the main drivers for the efficiency of the pure bundling mechanism and allow the sellers to easily quantify the potential profit of a large‐scale bundling mechanism relative to more complicated selling mechanisms. Our numerical simulations show that these analytics provide high predictive power for the true performance of the bundling mechanism and are robust to different parametric assumptions even for relatively small bundles. For example, for a bundle of 1200 and 12,000 items, the medians of the prediction error of our tight bound are −0.021 and 0.041 while the interquartile ranges are 0.094 and 0.067, respectively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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