Technologies numériques et transformations des industries : considération des effets de la numérisation du service des transports publics individualisés en France
Bibliographic record
Abstract
En France, la transformation des transports publics particuliers de personnes (T3P) s’accompagne de l’apparition des plateformes numériques, autour desquelles se développent de nouveaux services de mobilité. L’expansion de ces nouveaux acteurs et les spécificités de leur modèle économique sont à l’origine de nombreuses transformations. Elles éprouvent les fondements traditionnels de l’économie des activités concernées, la structure de l’industrie et celle du marché. Les dysfonctionnements inhérents à ces évolutions ont des répercussions sur les conditions socio-économiques des chauffeurs et sur l’agencement des métiers. Cet article appréhende les effets de la numérisation des services dans le secteur des transports individualisés. Il montre comment l’agrégation des ressources technologiques et les transformations de l’environnement politique et économique contribuent à l’émergence d’un nouvel ordre dans lequel se déploient des pratiques commerciales qui affectent la structure des métiers, les relations d’emploi et les conditions de travail.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".