Disuguaglianze nella durata della vita per grado d'istruzione in Italia all'inizio degli anni 2000
Bibliographic record
Abstract
This paper presents measures of differential mortality in Italy by educational level. The results refer to the year 2001 because the census is the only source providing data about population by level of education; as to deaths the data are provided by death certificates. As often happens, in order to compute differential mortality using these period frequencies it was necessary to confront problems both in the use of unlinked records and in relation to limitations in statistical documents; that is why we classified the population in only two groups: one with a low level of education and the other with a high level. We used a logit relational method to build life tables according to the levels of education. Particular attention was given to expectation of life at the ages from 35 to 65. At 35 years of age the expectation of life for a person with a low level of education is about 7.5 years less than for a person of a higher level in case of a male, and 6.5 years less in case of a female. The tendency continues, and at the age of 65 the expectation for those with a lower level is one quarter less in case of males and one fifth less in case of females. We found that a linear relationship exists between life expectancy and standardized rates among the Italian provinces; the same relation is also true for the analogous indicators of differential mortality by educational level.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".