Just Released: Benchmark Revisions Paint a Brighter Picture of (Most of) the Regional Economy
Bibliographic record
Abstract
Every March, the Bureau of Labor Statistics releases benchmark revisions of state and local payroll employment for the preceding two years. While employment data are released monthly for all 50 states and many metropolitan areas, the monthly figures are estimated based on a sample of firms. The annual revisions are based on an almost complete count of workers (now available up through mid-2014) from the records of the unemployment insurance system and re-estimated data for the remainder of the year. In this post, we briefly summarize the mixed but mostly stronger performance in the region in 2014 indicated by these employment revisions. We highlight the most pronounced changes across our District—highlighted by New York City’s even stronger-looking boom—using the percentage change in total employment from the fourth quarter of 2013 to the fourth quarter of 2014 as the metric.
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.046 |
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".