Mesure de la productivité morphologique des créoles : au-delà des méthodes quantitatives
Bibliographic record
Abstract
La plupart des mesures de productivité morphologique récentes sont des mesures quantitatives qui ne sont fiables que si elles portent sur de très grands corpus. Cet article propose la démonstration détaillée d'une méthode mise au point pour établir l'inventaire des affixes productifs d'une langue pour laquelle de tels grands corpus ne sont pas disponibles. Cette méthode évalue la productivité d'un affixe avant tout sur la base de son seuil de rentabilité (le nombre de mots différents dérivés au moyen de cet affixe), mis en corrélation avec d'autres diagnostics de productivité, de façon à augmenter la fiabilité de ce seuil. Ces autres diagnostics sont la transparence, sémantique et phonologique, des mots dérivés, ainsi que la décomposabilité des mots dérivés (confirmée par la présence d'un autre affixe productif à l'intérieur de la structure dérivée au moyen de l'affixe dont la productivité est évaluée). La démonstration est illustrée par étapes au moyen des données du saint-lucien.Most recent measures of morphological productivity are reliable only if they are based on a large corpus of the language. This article presents a detailed demonstration of a method for establishing an inventory of productive affixes in a language for which a large corpus is not available. This method evaluates the productivity of an affix first and foremost on the basis of its threshold of profitability (the number of different words derived via the affix) in correlation with other diagnostics to bolster reliability. These other diagnostics are the semantic and phonological transparency of derived words and the decomposability of such words. The application of themethod is illustrated step-by-step with data from St. Lucian.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".