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
Patch management is a crucial component of IT security programs. An important problem within this context is to determine how often to update the systems with necessary patches. Keeping the systems patched with more frequent patch updates increases operational costs while reducing security risks. On the other hand, leaving the systems unpatched with less frequent patch updates decreases operational costs while increasing security risks. In this paper we develop a game theoretic model to derive the optimal frequency of patch updates to balance the operational costs and damage costs associated with security vulnerabilities. We first analyze a centralized system in a benchmark case to find the socially optimal patch management policy and associated patch release cycle of the vendor and patch update cycle of the firm. Then we consider a noncentralized system in which the vendor determines its patch release policy and the firm selects its patch update policy in a Stackelberg framework. Given the results in centralized and noncentralized patch management, we next address how we can coordinate the patch release policy of the vendor and the patch update policy of the firm using cost sharing and/or liability to achieve the socially optimal patch management in a noncentralized setting.
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 | 0.000 |
| 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