Labour Force Survey 2022, 1st Quarter
Bibliographic record
Abstract
The Norwegian Labour Force Survey (NLFS) began in 1972 and shows how many people are employed, unemployed or outside the workforce. It provides information on, among other things, occupations, working hours and working time arrangements for those who are employed, search length for the unemployed, and age and education for all. In 2021, many changes were implemented in the NLFS. The sampling plan was changed to better reflect the main output goals for the survey. It included moving to an individual sampling unit, allowing new stratification variables and allocations. The weighting methodology for the NLFS was changed together with the practical and technical details behind the calculation of monthly and quarterly individual weights and yearly household weights. The number of persons in employment increased by 22 000 and the number of unemployed increased by 5 000, as a consequence of the new EU LFS regulation. In connection with the survey's 50th anniversary in 2022, an article was published about why and how the NLFS was established in 1972: https://www.ssb.no/arbeid-og-lonn/sysselsetting/statistikk/arbeidskraftundersokelsen/artikler/hvordan-arbeidskraftundersokelsen-ble-til
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.051 |
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".