Bibliographic record
Abstract
According to the ‘International Standard Classification of Education’ (ISCED), ‘Continuing Education’ is composed of the ‘Continuing Primary and Lower Secondary Adult Education’ (ISCED1, ISCED2), followed by the ‘Adult Education and Training’ System (ISCED3), including an ‘Upper Secondary Education System’ (ISCED4 and finally, the ‘Tertiary Education’ (ISCED5, ISCED6 and above). In 2016 the percent of ‘early leavers’ from education and training amounted in Italy to 13.8%, while the EU average amounted to 10.7%. In the same period the attendance to ISCED1-ISCED3 Adult Education Courses (age 25-64) amounted to 8.3%, while the EU average amounted to 10.8%. As for ‘Tertiary Education’, the percent attainment of a university degree amounted to 29.5% among Italians and to 13.4% among foreigners residing in Italy, while the EU averages amounted to 39.9% and 35.4% respectively. According to the Author, the relatively higher percent of early leavers from education and training in Italy and the relatively low attendance to ‘Continuing Education’ programs is due to the low employment rate in the Country, particularly significant in the age range 20-34, as a consequence of the severe economic crisis which hit the country in 2008 and still persists, causing the closure of many private enterprises and the block of the turn-over at public educational institutions. In spite of all that, the quality of the Italian Primary, Secondary and Tertiary Education System is of an excellent level, as compared with that of similar institutions all over the world. In Author’s opinion, increasing the investment in the educational system would increase the attendance to Secondary and Tertiary Adult Education courses, with a positive feedback on productivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".