Rendre à César ce qui lui appartient : l’utile hypothèse d’un droit moral des données
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Je propose d’explorer dans cet article en quoi l’hypothese d’un droit moral des donnees creees par l’internaute peut contribuer a l’actuelle reflexion sur la regulation des donnees massives et de l’intelligence artificielle. C’est donc d’un article prospectif qu’il s’agit ici, puisque je cherche a proposer une solution pertinente au defaut d’agentivite de l’internaute quant aux manifestations de sa vie en ligne, defaut dont les nombreuses consequences negatives ont ete largement presentees ces dernieres annees (manipulations electorales, bulles de filtre, campagnes de persecution, ciblages economiques, voire poursuites judiciaires mal fondees). En tant que composante extrapatrimoniale du droit d’auteur, le droit moral se scinde en differentes branches, que la loi canadienne sur le droit d’auteur synthetise en deux points principaux : un droit d’attribution (ou d’anonymisation) et un droit a l’integrite de l’oeuvre. Applicable aux donnees grâce au regime de la compilation, ce duo offre des perspectives non negligeables pour enrichir le debat sur la regulation des donnees et de l’intelligence artificielle.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it