25 years of the Labour Force Survey in Poland — milestones and development prospects
Bibliographic record
Abstract
The Labour Force Survey (LFS) is one of the basic survey conducted by the CSO. It enables current evaluation of the use of labour resources and at the same time it allows for a wider characterisation of population groups due to their status on the labour market. In 2017, a quarter of a century has passed from the time of the first edition of the LFS, which, since the very beginning, has been implemented in accordance with international recommendations and modified regarding the needs of data users. The beginnings of LFS in Poland are closely related to the period of systemic transformation and the demand for research allowing to fill the information gap concerning the possibilities of characterisation of new phenomena on the labour market. Following the accession of Poland to the European Union (EU), data from the survey became the basis for compilation of key indicators used as the essential ones in various strategies, both at the EU and national level. The aim of the article, apart from the jubilee theme, is to recall the milestones and the most important changes in the LFS methodology, which is extremely important for data users. Moreover, work conducted in this field within the EU is described in the final part of the article.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".