The long-term preservation of the digital heritage: the case of universities institutional repositories
Bibliographic record
Abstract
L'articolo affronta le tematiche legate ai problemi della conservazione a lungo termine del contenuto degli archivi digitali. Il materiale d'archivio richiede un'attenzione speciale ad aspetti quali la credibilità, il valore giuridico, i diritti morali e legali e la privacy. La necessità di assicurare accessibilità e integrità ai dati informatici è tuttavia una problematica che attraversa tutti i campi dell'informatizzazione ed è strettamente legata a fattori come la frequente duplicazione e la corretta scelta dei metadadi. Attraverso l'analisi del caso di studio rappresentato da cIRcle, l'istitutional digital repository della University of British Columbia (UBC), il contributo mostra problemi, rischi e soluzioni utili nella gestione di un archivio digitale, mostrando che l'esperienza degli archivisti può essere utile per sviluppare sistemi legati a depositi di informazione non prettamente archivistici.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.034 | 0.020 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".