Löhne und Beschäftigung: Was wissen wir mehr als vor 25 Jahren? (Wages and employment * what more do we know than 25 years ago?)
Bibliographic record
Abstract
"This article, which serves as the introduction to the special issue focusing on 'wages and employment', deals with the key developments of the past 25 years in this area of research. First, the latest insights and findings of macro- and microeconomic theory as well as of empirical knowledge on the explanation and quantification of the relationship between wage level, wage structure and employment as well as of employment structure are the centre of attention, with the relevant progress in econometry also being acknowledged. Then, the importance of wage rigidities is dealt with, which are significant both from a macro- and a microeconomic point of view, and which have been at the centre of scientific and economic-policy discussions particularly in recent years. Finally, wage-policy conclusions are examined briefly. What becomes obvious is that a negative relationship between wages and employment can be detected far more clearly nowadays than a quarter of a century ago. This is equally true on the basis of theoretical and empirical analysis with a micro- and macroeconomic background, even if, when based on disequilibrium models, for the latter no positive employment effects due to reductions in real wages can be expected in times of recession." (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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".