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
Value at risk (VaR) is now universally used to measure and manage exposure to financial market risk. Yet its shortcomings are also well known, one of the most serious being that it is susceptible to being “gamed.” Since VaR focuses only on the loss at a single probability quantile, like 1%, it presents the opportunity for a tricky operator to inflate his returns by taking on unmeasured extreme risk exposure in the tail beyond the VaR cutoff, as the authors illustrate in the first part of this article. Use of the related concept of “conditional tail expectations” or “tail VaR” can mitigate this problem. But a second difficulty that is not addressed by tail VaR is that by focusing on a single horizon, the VaR methodology ignores how loss probabilities may evolve over the short run. For this, the authors offer “iterated conditional tail expectation,” a new dynamic risk measure that is based on repeated monitoring of tail VaR over the forecasting horizon.
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 | 0.002 | 0.002 |
| 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) | 0.001 | 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