Reform of Severance Pay Law Statements and Comments
Bibliographic record
Abstract
1.1 Labour market situation Estonia is commonly characterised as a small open economy. The total number of employed persons in Estonia amounted to 586 300 persons in 2005 (data of the Labour Force Survey). The employment rate was 64 % in age group 15-64. The unemployment rate in the third quarter of 2006 dropped to 5.4%, which is the lowest level in the last decade. Employment situation has steadily improved over the last 5 years (starting from 2000), backed by strong economic growth. High economic growth rates – in a range of 7-8 % annually over the period 2000-2004 and reaching 10.5 % in 2005 – have recently lead to a shortage of labour force. Considering the relatively strict immigration rules towards third country nationals and still relatively low attractiveness for intra-Community labour migration, this has resulted in a high competition for labour between companies and has fuelled wage growth in last years. However, recent data (after 2001) on labour turnover is lacking. Masso et al (2004) estimated that gross job flows (job creation and destruction) in 1995-2001 amounted to nearly 25 % of total employment per year. This would mean about 70 thousand new jobs created annually and about the same number destroyed. Higher jobs flows were observed in micro firms employing less than 10 employees and
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.023 | 0.015 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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".