Employment Patterns During the Recovery: Who Are Getting the Jobs and Why?” Federal Reserve Bank of Kansas City, Economic Review. Fourth Quarter
Bibliographic record
Abstract
Employment in the United States is recovering slowly from the Great Recession. After declining by 8 million in the economic downturn, the number of employed workers has increased by only 1 million thus far in the recovery. Economists have offered different explanations for this sluggish recovery. Some have posited that the labor market has been weighed down by a mismatch of the jobs being created in the recovery and the available workers. By extension, this reasoning sees the labor market as imposing a constraint on overall growth. Others have argued that cycli-cal factors are responsible: The weak recovery in the broad economy is holding back the labor market, instead of the reverse. This article examines the distribution of employment gains in the recovery to shed light on this issue. It finds that employment growth has differed sharply depending on workers ’ level of education, age, and gender. Workers with high levels of education, workers age 55 and older, and men have experienced the strongest employment gains in the recovery. Workers with less than a high school education and workers ages
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".