Bibliographic record
Abstract
Abstract Polysemy, even when it is considered within specialized domains, is a recurrent phenomenon and the topic is debated from time to time in terminology literature. Part of this literature still advocates ways to prevent polysemy. Another portion recognizes the prevalence of polysemy, especially in specialized corpora, but considers it from the perspective of other phenomena, such as ambiguity, indeterminacy, categorization or variation. Although the number of perspectives on meaning have increased over the years, the treatment of polysemy in terminological resources is still unsatisfactory. This article first shows that polysemy is an integral part of specialized communication and that there are different kinds of domain-specific polysemy. Then, it reviews selected perspectives that have been taken on polysemy in terminology literature. The treatment of 45 polysemous lexical items in four specialized resources is then analysed. Finally, different methods based on lexical semantics are proposed to account for polysemy in terminological resources.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".