L’apprendimento permanente in Italia. Le scommesse del passato, la krisis del presente, le sfide del futuro. Un editoriale
Bibliographic record
Abstract
Il numero che presentiamo fa riferimento al Convegno "L’apprendimento permanente in Italia. Le scommesse del passato, la krisis del presente, le sfide del futuro. Dieci anni di RUIAP che ha celebrato i dieci anni di impegno nazionale della Rete che collega le Università che, a livello di Alta Formazione, in Italia, lavorano per tenere vivo e diffondere la sfida dell’apprendimento permanente. Il Convegno ha posto all’attenzione della comunità scientifica il tema centrale dell’apprendimento per tutti e per tutto l’arco della vita, un diritto di ogni persona umana, sancito in Italia dalla Legge 92 del 2012. Come sappiamo, con l’articolo 4 (commi 51-68) della Legge 92/2012, l’Intesa in Conferenza Unificata del 20 dicembre 2012 e l’Accordo in Conferenza Unificata del 10 luglio 2014, è stato istituito e disciplinato nel nostro Paese l’apprendimento permanente (MIUR, 2022).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".