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 studies the optimal insurance problem within the risk minimization framework and from a policyholder’s perspective. We assume that the decision maker (DM) is uncertain about the underlying distribution of her loss and considers all the distributions that are close to a given (benchmark) distribution, where the “closeness” is measured by the L2 or L1 distance. Under the expected-value premium principle, the DM picks the indemnity function that minimizes her risk exposure under the worst-case loss distribution. By assuming that the DM’s preferences are given by a convex distortion risk measure, we disentangle the structures of the optimal indemnity function and worst-case loss distribution in an analytical way, and provide the explicit forms for both of them under specific distortion risk measures. We also compare the results under the L2 distance and the first-order Wasserstein (L1) distance. Some numerical examples are presented at the end to illustrate the implications of our main results.
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 | 0.033 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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