Approaching Metaphorical Terms in Subject-specific Terminologies (Geologic and Geodetic): Semantic and Structural Aspects
Bibliographic record
Abstract
Terminological metaphors appear as a result of terminologisation, the process when commonly used words acquire special meaning, specific to a certain area of science. The mechanism of metaphoric representation of subject-specific concepts is based on certain associations (form, shape, function, structure, etc.). Terminological metaphors constitute an essential part of professional vocabulary being the means that help to facilitate nomination and understanding of subject-specific concepts, objects and processes for both specialists and non-professionals. This paper examines metaphorical terms in the domain of two subject-specific terminologies – geodetic and geological. The main objective of the research is to analyze terminological units of metaphorical character extracted from dictionaries and related reference literature in geodesy and geology, to determine their structural and semantic peculiarities, as well as their productivity. Metaphors under study have been investigated in accordance with semantic, structural and morphological approaches. The quantative analysis and the method of calculations have been applied to establish the productivity of different semantic and structural models of the terms. It has been found out that anthropological metaphorical terms prevail in geodetic and geologic terminologies. The vocabulary under study comprises one-stem terms, compound terms and terminological word combinations of metaphorical character. The latter ones turned out to be dominant. The results of the data analysis indicate that the number of noun models exceeds in both terminological systems.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".