Quarterly Labour Force Survey Eurostat Dataset, Quarter Three, 2008
Bibliographic record
Abstract
The Quarterly Labour Force Survey Eurostat Datasets form the UK component of the European Union Labour Force Survey (EU LFS), and consist of a subset of core variables from the UK Quarterly Labour Force Survey (held at the UK Data Archive under GN 33246), alongside primary and secondary derived variables computed by Eurostat from the core variables supplied. The data comprise seasonal or calendar quarters, depending on the date, and are not directly comparable with the UK QLFS quarters. Annual EU LFS datasets from 1999 onwards are also available (see under GN 33399) and 'ad hoc' modules (run each year to supplement the information from the core EU LFS questionnaire) are available from 2002 onwards (see under GN 33400). Users should note that the LFS Eurostat datasets available from the UK Data Archive comprise UK data only, and no data from other EU countries are included here. Further information about the EU LFS can be found on the Eurostat EU Labour Force Survey webpage. The UK Labour Force Survey (LFS) is a unique source of articulated information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. The first LFS was conducted in 1973 and continues to be one of the reasons for carrying out the survey. Eurostat co-ordinates information from labour force surveys in the European Union (EU) member states in order to assist the EU in such matters as the allocation of the Social Fund. Between 1984 and 1991 the survey was carried out annually, and moved to a quarterly cycle (the QLFS) from May 1992. Further information may be found in the main LFS documentation (see link below). LFS Documentation (main LFS) Besides the EU LFS documentation (see below), documentation is also available to accompany the main UK LFS datasets available from the Archive. This largely consists of the latest version of each document alongside the appropriate questionnaire for the year concerned. However, LFS documentation volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.119 |
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".