Labour Force Sample Survey 1975, 2.quarter
Bibliographic record
Abstract
Statistics Norway has carried out official quarterly Labor Force Surveys (AKU) from 1 quarter 1972. The purpose is to provide the labor authorities and others with the occupational structure of population and development over time. The surveys will provide the basis and statistical material for occupational forecasting and labor market research. In the AKU respondents are first interviewed in two consecutive quarters, then there is a delay of two quarters, and then the respondent are interviewed again for two consecutive quarters. The sample was approximately 10- 11,000 respondents each quarter until 1988. Labour Force Survey 1975 is the fourth complete annual AKU. Originally it was intended that the AKU would be an analytical supplement to the monthly employment statistics which were based on the insurance fund membership. The health insurance-based statistics, however fell away when health insurance was included in the National Insurance Scheme from 1 January 1971. AKU has gradually evolved to become the main source of knowledge about the labor market of the state. In 1975 the SSB on the sampling plan for the surveys, see Art.37: About the use of samples at the office for interview surveys, Statistics Norway (Steinar Tamsfoss), and SES 33: Sampling Methods Applied by Statistics Norways sample surveys (Ib Thomsen). Meanwhile, the method of estimation for inflating the national figures changed, so that there are adequate numbers of regions from 1975. The reorganization in 1975 meant that the interview was carried out differently in groups than before and after. This meant, among other things, a break in the panel structure of the AKU.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.030 |
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".