Bibliographic record
Abstract
There has been no robust growth of the low-pay sector in Germany since 2006. Over the past few years, a constant 22 percent of all employees have fallen into this category. The job structure within the low-pay sector has not changed in the last decade. In the economy as a whole, however, there has been less and less demand for low-skilled work, which is increasingly becoming concentrated in the low-pay sector. The low-pay sector include many people in part-time and, in particular, marginal employment. Only half of them are in full-time employment. As a result of low hourly rates, they accept long working hours so as to be able to earn a reasonable living. Those in full-time employment in the low-pay sector work an average of almost 45 hours a week, and a quarter of them 50 hours or more. However, this does not go very far towards compensating for the disparity between their pay and average monthly earnings. Working hours comparable to those of low-wage earners are otherwise only seen at the top end of the pay scale, in other words, among high earners in full-time employment. The majority of part-time workers, particularly those with mini-jobs would like to work more and earn more; a hidden underemployment is evident here. Working in the low-pay sector does not automatically or normally go hand in hand with social welfare benefits; only one in eight of low earners are Hartz IV benefit recipients. The proportion of people in full-time employment in the low-pay sector is particularly small; they only claim state benefits if they have to provide for a larger family. And only a minority of low-wage earners in part-time work or with mini-jobs receive social welfare benefits. There are normally other people living in their household who are in employment, or there is another source of income such as a pension or private support payments.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.116 | 0.030 |
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".