Online Appendix Not For Publication 1 Further Model Details 1.1 Unemployment Insurance
Bibliographic record
Abstract
We assume that unemployment benefits are paid only for the quarter immediately following job destruction. Unemployment insurance is paid only to people who have worked in the previous period, and only to those who had their job destroyed (job quitters are ineligible for UI payments, and we assume this can be perfectly monitored). We assume Bit = b × wit−1h, subject to a cap, and we set the replacement ratio b = 75%. This replacement ratio is set at this high value because the payment that is made is intended to be of a similar magnitude to the maximum available to someone becoming unemployed. This simplifying assumption means that, since the period of choice is one quarter, unemployment benefit is like a lump-sum payment to those who exogenously lose their job and so does not distort the choice about whether or not to accept a new job offer. Similarly, there is no insurance against the possibility of not receiving a job offer after job loss. 1.2 Universal Means-Tested Program The universal means-tested program is an anti-poverty program providing a floor to income for all individuals, similar to the actual food stamps program but with three important differences. First, means-testing is on household income rather than on income and assets; second, the program provides a cash benefit rather than a benefit in kind; and third, we assume there is 100 % take-up. 1 Gross income is given by y gross it = withPit + BitE UI it
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.496 | 0.131 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".