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
We use survey data on an opt-in panel of around 2,500 US small businesses to assess the impact of COVID-19. We find a significant negative sales impact that peaked in Quarter 2 of 2020, with an average loss of 29% in sales. The large negative impact masks significant heterogeneity, with over 40% of firms reporting zero or a positive impact, while almost a quarter report losses of more than 50%. These impacts also appear to be persistent, with firms reporting the largest sales drops in mid-2020 still forecasting large sales losses a year later in mid-2021. In terms of business types, we find that the smallest offline firms experienced sales drops of over 40% compared to less than 10% for the largest online firms. Finally, in terms of owners, we find female and black owners reported significantly larger drops in sales. Owners with a humanities degree also experienced far larger losses, while those with a STEM degree saw the least impact.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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