What can Verbs and Adjectives Tell us about Terms ?
Why this work is in the frame
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Bibliographic record
Abstract
Corpus processing tools are now an integral part of the compiling of specialized dictionaries and updating of term banks. They have led terminographers to consider terminological data differently, since many regularities and problems are highlighted in a more systematic manner. One linguistic fact more immediately revealed by the use of corpus tools is the relationship between terms in noun form with verbs and adjectives. In this paper, we study two specific types of relationships, namely morphological and syntagmatic relationships. We propose to consider lexical units that have one of these relationships with terms in nominal form. We will demonstrate that verbs and adjectives should be taken into account by terminographers for a number a reasons: some of them provide clues to the meaning of terms, others are morphologically and semantically related to terms in noun form.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it