Introduzione ai Dati Linguistici: Standard e Archivi Digitali
Bibliographic record
Abstract
Il corso "Introduzione ai Dati Linguistici: Standard e Archivi Digitali" introduce gli insegnanti e gli studenti all'uso degli archivi digitali di dati della ricerca e al loro ruolo nel ciclo di vita dei dati linguistici nel contesto degli principi FAIR e delle buone pratiche della Scienza Aperta. I materiali del corso sono suddivisi in unità e sono intesi come contenuti didattici per i docenti che insegnano a livello di laurea triennale o laurea magistrale, che sono invitati a sfogliare i materiali, esportarli per l'uso nel Learning Management System della loro istituzione e adattarli ai propri scopi come ritengono opportuno. Questo corso traduce in italiano e aggiorna i materiali di: van der Lek, Iulianna; Fišer, Darja. (2023). Introduction to Language Data: Standards and Repositories. In UPSKILLS Learning Content. https://upskillsproject.eu/project/standards_repositories/. CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ L'adattamento si è svolto nell'ambito del progetto Humanities and cultural Heritage Italian Open Science Cloud (https://www.h2iosc.cnr.it/), Work Package 8 "Training, Capacity Building, Engagement", a cura del personale CNR-ILC dedicato all'Attività 8.2 "Teach CLARIN, Teach with CLARIN". 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 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.033 |
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".