Termes complexes et langues de spécialité en sciences humaines et sociales : que nous apprennent les textes intégraux ?
Bibliographic record
Abstract
À partir d’un corpus d’articles scientifiques de trois disciplines de sciences humaines (archéologie, linguistique et psychologie), l’étude que nous présentons a pour objectif d’examiner la composition des candidats termes polylexicaux de forme N_Adj (langage humain, niveau individuel, origine sociale, structure syllabique). Nous nous concentrons sur les occurrences de candidats termes considérées comme relevant d’un usage disciplinaire par les annotateurs experts de chaque discipline. Nous nous intéressons, en particulier, aux classements possibles des noms et des adjectifs dans trois lexiques en interaction dans l’écrit scientifique : le lexique scientifique transdisciplinaire, les ressources terminologiques et le lexique de la langue générale. Les candidats termes analysés ont au moins un composant, le nom ou l’adjectif, qui appartient à au moins deux des trois disciplines du corpus. À l’issue de cette étude, nous constatons le rôle primordial du lexique disciplinaire et ses interactions fréquentes avec le lexique scientifique transdisciplinaire dans la formation des termes polylexicaux de forme N_Adj.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".