Multiple Perspectives on Terminological Variation
Bibliographic record
Abstract
The aim of the present volume is to provide a present-day take on variation in terminology by looking forward and examining what leading scholars in the field are working on and where they are taking research in the field today.This reader is built around three themes arranged according to complementary points of view to stimulate thought on the subject of variation as it is approached today. The first theme, “The social dimension of variation”, includes three contributions dealing with variation across different categories of speakers. This reflects not only the expert/layperson dichotomy but also other more original polarities as the emotional dimension and the issue of diastratic variation across LSPs. The second part of this reader puts forward different tools and methods to identify, describe and manage term variation. The third theme of this reader questions semantics of term variation through the topics of concept saturation, multidimensionality and metaphor.Variation, through this picture of current studies, proves to be the touchstone for the understanding of the major issues of terminology research today. The included papers draw on research in terminology carried out in different language communities - Spanish, French, Portuguese, Italian and Dutch in particular - thereby opening up a window on much of the research carried out in these cultural areas.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".