Problem nisko opłacanych pracowników w Polsce na tle innych krajów gospodarki rynkowej
Bibliographic record
Abstract
Research which has been carried out in countries with developed market economies indicates that since the 1980s there has been a systematic increase in wage inequality. As a result, low-wage employment has increased. In recent years, many academic institutions in developed countries have been conducting research into the scale, causes and consequences of low wages. Such research has not as yet been systematically carried out in Poland. This article aims to contribute to the research. Its objective is to establish the scale and causes of low-wage employment in Poland in the last sixteen years. The contents of the article are divided into three parts. The first part is devoted to the methodology of measuring low wages. The second part concerns the frequency of low wages in Poland and other countries with market economies. The third part of the article presents factors influencing the scale of the occurrence of low wages. According to the research, the number of people in low-wage employment in Poland systematically increased in the period from 1989 to 2004. In 2004 this number exceeded 22% of all working people. A similarly high percentage of low-wage employment is found only in Korea, Hungary, Great Britain, the USA and Canada.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".