He was shot dead / he was shot to death : l'apport de la traductologie à l'analyse unilingue
Bibliographic record
Abstract
Le présent article a pour objectif de mettre en lumière l’apport de la comparaison avec le français pour l’analyse d’un fait de langue en anglais, à savoir l’alternance des groupes adjectivaux et prépositionnels en to dans les constructions résultatives du type he was shot dead / he was shot to death. Pour des raisons liées aux difficultés matérielles engendrées par la constitution d’un corpus bilingue, l’étude ci-après s’appuiera sur des énoncés de l’anglais du Canada et leur traduction française, collectés via la base de données WeBiText . Quant aux énoncés anglais non traduits, ils proviennent du British National Corpus ou du Corpus Of Contemporary American English . L’analyse proposée s’inscrit dans le cadre de la Théorie des Opérations Énonciatives d’Antoine Culioli.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".