Bibliographic record
Abstract
La vie politique serait aujourd’hui caractérisée par une individualisation et une personnalisation des capitaux politiques, au détriment des partis. L’émergence de plateformes numériques accélérerait ce phénomène en permettant aux politiques une expression publique plus personnalisée. Cet article vise à saisir les modalités empiriques de cette proposition à travers l’analyse de la communication sur Twitter des députés de la XV e législature : en ayant recours à des outils de statistique de réseaux appliqués à plus d’un million de tweets récupérés entre 2017 et 2019, nous y étudions le poids des partis dans la structuration des interactions entre députés et les recompositions induites entre capitaux partidaires et individuels. Les partis encadrent encore largement la communication des parlementaires, ce qui n’est pas contradictoire avec une individualisation de leurs figures de proue : la communication collective des fractions parlementaires est enrôlée à leur bénéfice, quoique de façon inégale entre partis.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.071 |
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".