BLS Spotlight on Statistics: Fifty Years of Looking at Changes in People's Lives
Bibliographic record
Abstract
[Excerpt] The U.S. Bureau of Labor Statistics publishes new numbers each month or quarter on unemployment, job growth, productivity, prices, pay, and benefits. To answer some questions about the labor market and economy, we may need years or decades instead of months or quarters. For example, how many jobs do people hold in their lifetimes? How much of people?s lives do they spend working or looking for work? How many people ever marry or divorce or have children? We call surveys designed to answer questions like these ?longitudinal? surveys. Longitudinal surveys help us understand long-term changes, such as how events that happened when a person was in high school affect labor market success as an adult. This year we celebrate the 50th anniversary of the National Longitudinal Surveys, which have tracked the experiences of different generations of Americans. This Spotlight on Statistics looks at some measures from two of those generations?people born in 1957?64 and people born in 1980?84.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".