A Chartbook of international labor comparisons
Bibliographic record
Abstract
[Excerpt] This chartbook focuses on the labor market situation in selected countries for the most recent year available; some charts also show trends. Charts in sections 1-4 and section 6 include countries in North America (the United States, Canada, and Mexico) and selected Asian-Pacific and European economies. Some countries do not appear on all charts due to the lack of suitable data. It should also be noted that the selected economies are not representative of all of Europe and the Asian-Pacific region; rather, they tend to be the more industrialized economies in these regions. Weighted aggregates for 15 European Union countries (EU-15) also are shown on many of the charts in these sections. These represent European Union member countries prior to the expansion of the European Union to 25 countries on May 1, 2004, and to 27 countries on January 1, 2007. The EU-15 countries are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom. In section 5, several indicators are presented for six large emerging economies: Brazil, China, India, Indonesia, the Russian Federation, and South Africa. The appendix describes the definitions, sources, and methods used to compile the data in the chartbook. For some series, the appendix provides cautions about the exact comparability of the measures.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.044 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.067 |
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".