Bibliographic record
Abstract
Cet article traite d’un cas d’espèce dans le vaste domaine de la rhétorique éristique et la polémique, à savoir les amères et interminables controverses qui ne portent que sur la qualification – au sens juridique – d’un événement ou d’un individu, d’une entité politique. Il étend donc cette notion de droit pour aborder la question générale des controverses d’étiquetage dont il montre la récurrence quotidienne. Il s’arrête notamment à un exemple prégnant de controverses de qualification juridique utilisées par les historiens avec la catégorie de « génocide » et ses abus. Il aborde l’étiquetage accusateur et « diabolisant » et ses catégories bien connues en longue durée comme « fasciste », « totalitarisme » etc.
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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".