Labour Force Survey, 2025, 2nd Quarter
Bibliographic record
Abstract
Statistics Norway established the Labor Force Survey (LFS) in 1972, and it has been conducted quarterly ever since. The LFS measures the population's participation in the labor market and provides comprehensive information on unemployment, employment, people outside the labor force, temporary employees, underemployed and other subgroups that are not captured by register-based statistics. This makes the LFS one of the most important sources of information about conditions in the Norwegian labor market. Right from the start, the aim has been to ensure that the survey is comparable with similar surveys internationally. Today, the LFS is designed in accordance with the EU's statistical regulations to ensure consistent and comparable European statistics. The LFS data contains long time series, and although there have been some breaks in the time series due to changes in the questionnaire and data collection, the most central variables have been continuously included since the start. This makes it possible to present time series data for the employed, unemployed and people outside the labor force all the way back to 1972. The dataset consists of a single quarter, along with a quarterly weight. The dataset should be used for a quarterly average rather than an annual distribution. If an annual average is desired, separate datasets are available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".