Bibliographic record
Abstract
L'objectif de la confrence qui s'est droule Florence tait de runir des chercheurs autour d'une littrature politique originale : les catchismes politiques. L'enjeu tait de collecter ensemble suffisamment de caractres communs permettant de dfinir un genre du catchisme politique. Compte-tenu de l'extrme diversit dans le temps et dans l'espace des catchismes voqus au fil des interventions nous avons souhait nous interroger sur les conditions de production et d'utilisation de ces livres ainsi que sur la rhtorique employe dans le but d'esquisser une approche de sa rception. Le premier rsultat notable de cette confrence fut l'accord trouv autour de la dfinition suivante : un catchisme politique est un abrg d'une doctrine politique, gnralement rdig sous la forme d'une succession de questions et de rponses. Cette dfinition fait consensus, quelle que soit la taille du corpus envisag (une cinquantaine dans le cas des ouvrages italiens du Triennio tudis par Luciano Guerci, prs de 800 si l'on s'intresse la production franaise du long XIX me sicle que j'ai tudie). Les spcificits apparaissent lorsqu'on aborde cette littrature sous un angle chronologique ou bien si on en cherche l'extension gographique.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".