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 letter provides an overview of our recent work on COVID-19 testing mechanisms that appeared at EC'23. Large-scale testing is crucial in pandemics but resources are often prohibitively constrained. We study a scenario in which a population under lockdown utilizes a limited budget of tests to allow healthy individuals to resume in-person activities. Our work explores the optimal allocation of pooled tests in populations that are heterogeneous with respect to individual infection probabilities and utilities that materialize if included in a negative test (and being permitted to resume in-person activities). Non-overlapping allocations of tests, where no individual in the population is included in more than one pooled test, are both conceptually and logistically simpler to implement. We show that the welfare gain from overlapping testing over non-overlapping testing is bounded. Moreover, we design a heuristic mechanism for finding test allocations that is fast and empirically near-optimal. We also implement our mechanism in practice and provide experimental evidence on the benefits of utility-weighted pooled testing in a real-world setting. Our randomized trial at a higher education research institute in Mexico suggests that performance and mental health outcomes of participants under our testing mechanism are no worse than under the counterfactual of full access for individuals without testing.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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