Compte rendu de 'faux-semblants du Front national. Sociologie d’un parti politique' par CRÉPON, Sylvain, DÉZÉ, Alexandre, MAYER, Nonna, Paris, Presses de Sciences Po, 2015
Bibliographic record
Abstract
1ères lignes : Les faux-semblants du Front national s’inscrit dans le sillage du regain d’attention que suscite le Front national (FN) au sein des milieux académiques depuis l’accession de Marine Le Pen à la présidence du parti en janvier 2011. Réalisé sous la direction de Sylvain Crépon, Alexandre Dézé et Nonna Mayer, cet ouvrage collectif se présente comme une analyse des changements qu’aurait connus le FN mariniste, comparativement au FN lepéniste. En plus de l’introduction et de la conclusion rédigées par les directeurs, le livre s’organise en cinq parties, soit dix-neuf chapitres (p. 13-536) et propose également un repère chronologique des événements clés de l’histoire du FN de 1972 à 2015 (p. 545-557).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".