Opinion Regulation or Civic Dialogue? Seeking New Theo-Retical Frameworks for the Study of Digital Politics (Entre Régulation De L’Opinion Et Dialogue Avec Les Citoyens : À La Recherche De Nouveaux Cadres Analytiques Pour Étudier La Communication Politique En Ligne)
Bibliographic record
Abstract
English Abstract: This article centers on the theoretical articulation of scientific studies examining interactions between different civic, political and economic actors implicated in discussions or informal exchanges among citizens on political parties’ social network sites (SNS). To come to a broader understanding of this phenomenon in western democracies, this text intends to contribute to the development of new, more nuanced, interdisciplinary, and generalizable analytical frameworks.French Abstract: Cet article s’interesse a l’articulation theorique d’etudes scientifiques qui portent sur les interactions entre acteurs sociaux, politiques et economiques impliques dans des discussions ou echanges sur les pages et comptes de reseaux socionumeriques (RSN) offerts par des partis politiques. Pour mieux saisir ce phenomene au sein des democraties occidentales, ce texte vise a contribuer au developpement de nouveaux cadres analytiques plus nuances, interdisciplinaires et generalisables.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".