Dembi‘nska (Magdalena) – La fabrique des États de facto. Ni guerre ni paix . – Montréal, Presses de l’université de Montréal, 2021 (Politique mondiale). 296 p. Bibliogr. Tableaux. Cartes. Figures.
Bibliographic record
Abstract
RésuméCet article propose une nouvelle perspective d’étude de la carte électorale, fondée sur la computational social science, pour analyser les critères qui encadrent la réalisation du découpage électoral en France. La délimitation des circonscriptions législatives est complexe en raison de sa dimension technique et des critères juridiques, démographiques et géographiques à respecter. À partir des données démographiques et géographiques des territoires et de la mesure de l’ensemble des découpages admissibles, nous évaluons les contraintes formées par les critères sur les potentialités empiriques de la carte électorale et, plus largement, nous montrons l’intérêt d’avoir recours aux découpages assistés par ordinateur.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.015 |
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".