Populisme dans les commentaires sur YouTube : entre dimension conflictuelle et enjeux argumentatifs
Bibliographic record
Abstract
Dans cet article, j'analyse les débats métadiscursifs autour du mot populisme dans les commentaires YouTube publiés entre 2015 et 2020 à la suite de vidéos où des spécialistes débattent autour du phénomène du populisme.Mon objectif est de montrer que bien que populisme soit considéré par les spécialistes comme une insulte fonctionnant comme étiquette polémique utilisée essentiellement pour dénigrer autrui, il fait l'objet, dans les échanges ordinaires, de remarques métadiscursives qui peuvent nuancer cette charge polémique jusqu'à l'effacer.Dans ce but, je me concentrerai sur trois pratiques métadiscursives ordinaires qui caractérisent le corpus, à savoir les actes de nomination, les lexicographismes, parmi lesquels on trouve différents types de resignification, et, enfin, les énoncés définitoires spontanés.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".