La logométrie en histoire: une herméneutique numérique. Exploration d’un corpus de professions de foi électorales de député-e-s (1958–2007)
Bibliographic record
Abstract
For historians dealing with text, the changeover from paper to digital sources represents a major change. By expanding text archives – turned into hypertexts, un-linear and denaturalised –, the digital increases the navigation capabilities within corpora, reveals new linguistics aspects that can be observed, and transforms our reading practices. Logometry, the method using computer-assisted reading, has been contributing to these evolutions over the last four decades by proposing tools that combine qualitative reading and quantitative approaches to digital corpora, with the goal of formalising the hermeneutic interpretive process. This article will present its principle functions through its application to a corpus of electoral manifestos written under the Fifth Republic (1958–2007). Résumé Le passage du papier au numérique constitue, pour les historien-ne-s ayant affaire aux textes, une rupture majeure. En enrichissant les archives textuelles – devenues des hypertextes, délinéarisés et dénaturalisés –, le numérique augmente les capacités de navigation dans les corpus, révèle de nouveaux observables linguistiques et transforme nos pratiques de lecture. La logométrie, méthode de lecture assistée par ordinateur, participe depuis quatre décennies à ces évolutions en proposant des outils alliant lecture qualitative et approche quantitative de corpus numériques dans le but de formaliser des parcours interprétatifs heuristiques. Cette contribution en présentera les principales fonctionnalités, appliquées à un corpus de professions de foi électorales rédigées sous la Cinquième République (1958–2007). Mots-clés: Logométrie; corpus numérique; herméneutique numérique; discours électoral; histoire du discours politique
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".