Bibliographic record
Abstract
Comparative unemployment rates are used frequently in international analyses of labor markets and are cited often in the press. In the United States, the comparative levels are considered to be an important measure of U.S. economic performance relative to that of other developed countries. Comparative unemployment rates also provide a springboard for investigating the economic, institutional, and social factors that influence cross-country differences in joblessness. The Bureau of Labor Statistics (BLS, the Bureau) has adjusted foreign unemployment rates to U.S. concepts since the early 1960s. Three other organizations—the Organization for Economic Cooperation and Development (OECD), the International Labor Office (ILO), and the Statistical Office of the European Communities (Eurostat)—also adjust national data on unemployment to a common conceptual basis. The resulting “standardized” or “harmonized” rates are intended to provide a better basis for international comparison than the national figures on unemployment offer. The standardized rates, as currently published by the three organizations that make comparisons outside of Europe (BLS, OECD, and ILO), all show a similar result: a significant gap in unemployment rates between the United States, on the one hand, and Canada and Europe, on the other. In 1998, for example, when International unemployment rates: how comparable are they?
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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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".