Bibliographic record
Abstract
Lesbig data, ledatamininget le profilage, avec l’ensemble des applications d’aide à l’action individuelle et collective qui en découle, suscitent à juste titre des inquiétudes en ce qui concerne, d’une part la protection de la vie privée dans un environnement capteur des faits et gestes de chacun, d’autre part les formes de gouvernance de plus en plus informées par des algorithmes prédictifs. Sans négliger ces dangers, une position presque inverse sera ici défendue sous forme d’hypothèse : loin d’entraîner le déclin de l’autonomie individuelle, de soi comme personne singulière capable de réflexivité et en position de faire des choix autonomes, la confrontation renouvelée à une image personnelle purement quantitative et utilitaire (profil) peut conduire à un ressaisissement de soi visant à ce que les choix soient non plus seulement guidés par une logique narcissique, utilitaire et quantitative, mais tout autant par des principes de cohérence individuelle, éthiques et moraux qui,in fine, donnent du sens à la vie.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".