In the South, more than a quarter of workers have remained in precarious jobs for years; the North holds steady but shows no improvement
Bibliographic record
Abstract
The analysis of data on employees who have held temporary jobs for at least five years between 2018 and 2023 provides deep insight into the structure and rigidity of the Italian labour market. This indicator measures the share of workers who, despite being employed, remain trapped for long periods in temporary or precarious contractual arrangements, unable to obtain a permanent position. It represents one of the clearest expressions of so-called structural precariousness, a phenomenon that in Italy takes on particular significance due to the fragmentation of the labour market and strong territorial disparities. The data depict a complex landscape: some regions have seen a slight increase in these workers, others a decrease, but one underlying constant remains—the number of people who stay in temporary jobs for years remains high, especially in the South, and although some territories show improvement, the situation remains a critical indicator of social and economic vulnerability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".