Environment terms and translation students
Bibliographic record
Abstract
Abstract This article reports on a pilot study that aims to shed some light on how translation students construe specialized terms. More specifically, we verified their ability to associate environment terms with specific conceptual situations (as understood by Frame Semantics [ Fillmore 1976 ; Fillmore and Baker 2010 ]). Respondents (27) were asked to complete a questionnaire containing 10 different questions that assessed the association of terms with conceptual situations from different angles. Results show that respondents can associate related terms and link sets of terms to conceptual situations and can make distinctions between the different components of conceptual situations when asked to produce lists of terms or select terms from a predefined list. However, when asked to assess the similarity or difference between specific terms, respondents are less likely to produce the anticipated answer. Our findings suggest that teaching and learning activities inspired by Frame Semantics may be helpful for students to structure their terminological analysis and deal with challenges such as ambiguity and fine semantic distinctions. We hope this can ultimately contribute to helping them make informed, precise and coherent terminological choices.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".