Labour Force Survey 2020, Year File
Bibliographic record
Abstract
LFS is the most important source of information about conditions on the labor market in Norway. After sample surveys in the autumn of 1971, Statistics Norway has from the 1st quarter of 1972 carried out such surveys quarterly according to the sample method. The purpose of the survey is to provide information on developments in employment and unemployment, and on the connection of different population groups to the labor market. In addition to providing the authorities and other interested parties with information on the state and development of the labor market, the LFS shall serve as a basis for forecasts and studies and provide research with statistical material. A large number of variables are included in the material. In addition to the demographics, mention can be made of education level, profession, industry, agreed and actual working hours, part-time employment and underemployment. Important users are the ministries, the labor and welfare administration, research and study institutes, international organisations, mass media etc. The labor force survey 2020, annual file is a collection file of the 4 quarterly files from 2020. The collection file also contains variables that deal with working time arrangements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.356 | 0.012 |
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; both teacher heads agree on what is shown here.
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".