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
Abstract We use a labor search model with heterogenous households and firms to study the efficacy of a wage subsidy during a pandemic, relative to enhancing unemployment benefits. A large proportion of the economy is forced to shut down, and firms in that sector choose whether to lay off workers or keep them on payroll. A wage subsidy encourages firms to keep workers on payroll, which speeds up labor market recovery after the pandemic ends. However, a wage subsidy can be costlier than enhancing unemployment benefits. If the shutdown is long or profit margins are low, then a wage subsidy is preferable and vice versa. The optimal mixture of policies includes a wage subsidy that covers 90 $\%$ of the first $200/week of earnings and expands unemployment benefits to cover all salary up to $275/week. Low-income workers, as well as those in less productive jobs, benefit the most from a wage subsidy.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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