Bibliographic record
Abstract
Contraiy to most reports, the economy is recovering, albeit slowly. It would be a mistake to act on the assumption that we are facing a deep recession or the start of another great depression. In retrospect, the recession will be seen as relatively mild. Our current unemployment rate at 6.8% of the labor force is below the rates in many countries including Britain (8.7%), Canada (10.3%), France (9.7%) or Italy (10%). Germany has had a boom for several years, but its unemployment rate at 6.3% is only slightly below ours. Economic forecasts are often wide of the mark. We should not add to our current problems by basing policy action on forecasts about the speed of the recovery. The best forecasts of economic growth have an average error about equal to the average rate of growth. A comprehensive study of forecast errors shows that forecasters cannot distinguish on average between booms and recessions next year or even next quarter. Basing policy actions on forecasts is likely to produce errors of timing.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.057 | 0.038 |
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".