Bibliographic record
Abstract
Spesso si scrive di digitale e di come il digitale stia cambiando la nostra vita, i nostri modi di accedere alle informazioni e alle narrazioni. Ma è anche vero che il più delle volte si parla di digitale usando strumenti e metodi di composizione fortemente analogici. Saggi, articoli, paper sul digitale presentano contenuti scritti e pensati per la pubblicazione su carta o PDF, che difficilmente ri-strutturano i propri materiali per una esperienza di lettura interattiva, modulare, dinamica.Questo breve contributo si presenta come meta-contributo: vuole presentare alcuni stimoli di rielaborazione delle idee e della loro formalizzazione digitale assieme a informazioni sui formati delle attuali pubblicazioni digitali. Ma il contributo stesso sfrutta, mostrandoli, alcuni di questi strumenti: navigazione ipertestuale, testi espandibili, grafici interattivi, video integrati nel testo, parti in continuo aggiornamento.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.018 |
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".