Bibliographic record
Abstract
Despite the US unemployment rate being close to estimates of the non-accelerating-inflation rate of unemployment (NAIRU), measures of underemployment remain elevated, which could be an indication of remaining labour market slack. The shares of involuntary part-time workers and long-term unemployment are high relative to the current stage of the business cycle, suggesting available labour inputs are being underutilized. Improvement in these areas could meaningfully increase US labour utilization and support economic growth. Another large potential source of labour market slack exists outside the labour force caused by the relatively low participation rate, which has fallen by more than 3 percentage points since 2007. Most analysis, including that in this note, finds that the aging population is an important factor behind this decline, indicating that there is less slack than implied by the drop in the headline participation rate. However, there is considerable uncertainty about whether the decline in participation unrelated to aging is driven by structural or cyclical phenomena and is therefore representative of slack. Nevertheless, a review of the historical experience suggests that a sizable number of persons outside the labour force could be “activated” and drawn back into the market under much “hotter” labour market conditions. But further research is needed to assess whether the historical relationship is a relevant guide in the current context.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".