WORKFORCE MOBILITY AGAINST THE BACKGROUND OF LABOUR MARKET DUALITY THEORY – THE EXAMPLE OF SELECTED OECD COUNTRIES
Bibliographic record
Abstract
The paper aims to present an empirical study of labour market segmentation (LMS) hypothesis. According to the dual labour market theory jobs can be divided into two groups: primary and secondary jobs, with enter barriers into the first one. The primary jobs are usually described with relative high wages, whereas secondary jobs provide lower level of wages. In this paper we first examine the main sectors (according to the ISIC rev. 3) in selected OECD countries, which are divided into two segments, regarding the level of average sectoral wages. Then, the intersectoral labour mobility within the secondary segment and the labour mobility from the secondary segment to the primary segment in every analysed country is measured for the years 1994-2008. A Markov chain analysis based on aggregate data is used to identify the differences in the workforce mobility and confirmed the existence of barriers on the segmented labour market. According to the main purpose of this paper the divided nature of labour market is verified. Our research show that in case of Finland, Greece, Spain, Canada, Portugal and United Kingdom, the divisions in labour market are substantial. On account of the significant differences in workforce mobility within and outside the secondary segment, we can indicate a typical duality in labour market in these countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".