Bibliographic record
Abstract
Bien que le lien entre innovation, standardisation et open source soit suggéré par différents exemples (p.ex. World Wide Web), il reste globalement peu exploré. Cette recherche propose donc l'exploration de ce lien. En particulier, elle discute les questions des types d'innovations amenés par les logiciels open source, du caractère innovant des logiciels open source, du lien existant entre standardisation et processus d'innovation open source, puis enfin de l'influence de la standardisation sur le caractère innovant des logiciels open source. Après avoir notamment montré les innovations juridiques, organisationnelles et de produit amenées par l'open source, nous proposons un premier classement des déterminants de l'innovation propres à l'open source puis identifions une première série d'objectifs pour les entreprises associant open source et standardisation. Nous posons enfin comme hypothèse le caractère non figé de ces déterminants et objectifs en termes de combinaisons et d'évolutions au fil du temps.
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 | 0.031 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".