Statistics in Focus: Population and social conditions. Main results from the EU Labour Force SurveyLabour Market Latest Trends –4th quarter 2004 data. 2005.6
Bibliographic record
Abstract
Main results from the EU Labour Force Survey Labour Market Latest Trends -4th quarter 2004 dataThis publication is the fourth of a quarterly series presenting the European Union Labour Force Survey main results for all Member States 1 .Changes in activity rate (15-64) Changes in employment rate (15-64) Changes in share of part-time employment (15+) Changes in share of temporary contracts (15+) Changes in unemployment rate (15-64) Men Women 1 The Federal Statistical Office of Germany provides quarterly estimates for the main variables until the German LFS becomes quarterly for 2005 onwards.The design of the continuous LFS in Luxembourg does not provide quarterly results for the moment.In this publication, as in the previous ones, the data have not been seasonally-adjusted, due to the short time series available.Therefore data have to be compared from one quarter to the same quarter one year before.2 Quarterly data are not available in Luxembourg.Due to the transition to a continuous survey in 2004, Austrian and Italian data for 2004 cannot be fully compared with the data one year before.3 The unemployment rate calculated in this paper is directly derived from the EU Labour Force Survey data.The adjusted unemployment series (15+) published on a monthly basis by Eurostat can differ from these results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.028 |
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".