TREND: United Nations Economic Commission for Europe. Gender Statistics [Archive]: Unemployment by Age | Country: Armenia, Belgium, Canada, France, Germany, Russia, Spain, Sweden, United Kingdom, United States | Selection 1: 25 - 49 | Selection 2: Female, Male | Selection 3: Unemployed, 1990 - 2007. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 054-003-027
Bibliographic record
Abstract
United Nations Economic Commission for Europe. Gender Statistics [Archive]: Unemployment by Age | Country: Armenia, Belgium, Canada, France, Germany, Russia, Spain, Sweden, United Kingdom, United States | Selection 1: 25 - 49 | Selection 2: Female, Male | Selection 3: Unemployed, 1990 - 2007. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 054-003-027 Dataset: Shows unemployment rate, by age and sex. The Gender Statistics database presents sex-disaggregated social data for the 56 member states of the United National Economic Commission on Europe (UNECE) region, which include the countries of Europe, but also Canada, the United States, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Israel. The data covers the Common Gender Indicators for the UNECE region as well as the data series that are used to calculate these indicators. The data have been supplied by national statistical offices through the network of Gender Statistics Focal Points, and are compiled by the Statistical Division of the UNECE Secretariat from different official national and international sources. Data available varies by country. NOTE: Data-Planet discontinued updating of this dataset in 2011 due to irregularities in the data structure. For more recent data on similar topics, please see the Eurostat database. http://w3.unece.org/pxweb/Dialog/ Category: International Relations and Trade, Labor and Employment Subject: Age, Gender, Unemployment Rates Source: United Nations Economic Commission for Europe The United Nations Economic Commission for Europe (UNECE) was established in 1947 as one of the five regional economic commissions of the United Nations. Its major aim is to promote pan-European economic integration. To do so, UNECE brings together 56 countries located in the European Union, non-EU Western and Eastern Europe, South-East Europe and Commonwealth of Independent States (CIS) and North America. All these countries dialogue and cooperate under the aegis of the UNECE on economic and sectoral issues. http://www.unece.org/
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.101 |
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".