Kurzarbeit in den neuen Bundesländern (Short-time work in the new federal states)
Bibliographic record
Abstract
"With the introduction of the Employment Promotion Act (GDR) on 01.07.1990, short-time working benefit was put to use as an employment and training measure in eastern Germany, too. In particular in the first year after reunification it contributed on a large scale to relieving the pressure on the labour market in quantitative terms: at times some 2 million people, that was at that time about a quarter of all those in employment in the new federal states, were working short-time with an average of over 50% loss of working time. Since then changes in regulations as well as economic and institutional adjustment processes have brought about a steady decrease. At the end of the first six months of 1996 the statistics still revealed some 61,000 recipients of short-time working benefit. Using statistical data and findings from the labour market monitor, this article provides an over-view of the utilisation and effect of short-time working in the new federal states. By drawing comparisons with the development in the old federal states, it is possible to show the convergence trend. At the same time, in connection with this some rather fundamental questions regarding the function of short-time working benefit are mentioned and experiences are reappraised." (Author's abstract, IAB-Doku) ((en))
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".