Progettare Materiali Didattici FAIR: L'esperienza di H2IOSC nella Gestione Condivisa di Oggetti Digitali Complessi
Bibliographic record
Abstract
La presentazione riassume la metodologia applicata dal progetto H2IOSC ai materiali di formazione diretti alla comunità italiana delle Social Sciences and Humanities. Dopo aver illustrato la strategia didattica del WP8 - "Training, Capacity Building, Engagement" la presentazione dettaglia le modalità di adattamento, creazione e gestione di materiali didattici intesi come oggetti digitali complessi attraverso l'applicazione della metodologia "FAIR-by-Design" sviluppata all'interno del progetto Skills4EOSC e l'utilizzo del "Minimal Metadata Set for Learning Resources" di Research Data Alliance. Infine, si presentano le piattaforme di training del progetto H2IOSC: il "Training Environment" per la gestione di eventi formativi e la "H2IOSC Library" per il deposito e il versionamento del materiale. Progetto H2IOSC - Humanities and cultural Heritage Italian Open Science Cloud, finanziato dall’Unione europea NextGenerationEU – PNRR M4C2 - Codice progetto IR0000029 - CUP B63C22000730005.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.019 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.046 |
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; both teacher heads agree on what is shown here.
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".