HAL et Software Heritage au service des bibliothèques
Bibliographic record
Abstract
Présentation réalisée préalablement au congrès 2024 de l'ADBU lors d'un webinaire destiné à présenter les offes de services de Software Heritage et du CCSD en matière de préservation et de diffusion des codes sources et logiciels de la recherche. Ce webinaire a également été l'occasion de revenir sur les enjeux de la préservation des codes sources et des logiciels de la recherche, sur la collaboration entre le CCSD et Software Heritage pour permettre le dépôt de logiciels dans HAL et sur les voies de dépôts proposées dans le cadre de cette collabraotion. Il s'est terminé par la présentation du programme conjoint conçu par le CCSD et Software Heritage pour amener les bibliothèques universitaires à s'emparer de cette question.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".