Bibliographic record
Abstract
The interest for economic insecurity has grown constantly over the last decade due to the shock of the latest global crisis that has involved the wealth and behavior of households thus attracting the attention of many authors in contemporary literature. Furthermore, the concept of economic insecurity may be linked to the concept of economic inequality, since an increase in economic inequality may be connected to insecurity and vice-versa. The aim of this article is to measure economic insecurity from 2012 to 2016 in Italy both at national and sub-national level. The methodology applied refers to the economic insecurity index suggested by Bossert et al. (2019) and the data considered are taken from the Survey on Households Income and Wealth carried out by the Bank of Italy. Moreover, this work aims to investigate the possible link between economic insecurity and economic inequalities, measured through the share of wealth owned by the richest 5% of the population. The main findings show a relevant general increase of economic insecurity in the period between 2012 and 2014, and interesting differences in the variation of economic insecurity at sub-national level. Insecurity appears closely linked to the level of inequality and the trend of inequality in the previous period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".