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
Rethinking Artificial Intelligence-Assisted Priority Queuing: The Cost of Locking in Classifiers When job types must be inferred from observable features, should artificial intelligence (AI) classifiers be locked before optimizing queue assignments, or should the full system be trained end to end? Singh, Gurvich, and Van Mieghem show that the modular “type-first” approach—locking a type classifier and then, optimizing queue assignment based only on its output distribution—can be fundamentally suboptimal. The authors establish that type-first achieves optimality only when the locked classifier recovers the Bayes posterior almost everywhere, a condition rarely satisfied in high-dimensional or misspecified settings. In contrast, their “direct” approach jointly optimizes feature-to-queue mappings to minimize empirical waiting costs and converges to the theoretical optimum. Using 100,000 chest X-rays from the National Institutes of Health, they show that direct optimization substantially improves waiting-cost performance and reveal mechanisms, such as statistical pooling and strategic overprioritization of medium-cost types. The work speaks to the growing debate over whether AI systems should be locked or adaptable.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.000 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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