Framing Terminology: A Process-Oriented Approach1
Bibliographic record
Abstract
The frame notion used in Frame Semantics can be traced to case frames, which were said to characterize a small abstract situation in such a way that if one wished to understand the semantic structure of a verb it was necessary to understand the properties of the entire scene that it activated. A frame has been more broadly defined as any system of concepts related in such a way that one concept evokes the entire system. In this sense, it bears an obvious affinity with terminology, which is also based on such conceptual organization. However, despite the fact that Frame Semantics has been usefully applied to lexicology and syntax, so far it has not been systematically applied to terminology. This paper argues for a frame-based organization of specialized fields in which a dynamic process-oriented frame provides the conceptual underpinnings for the location of sub-hierarchies of concepts within a specialized domain event, and the elaboration of a definition template, thus opening the door to a more adequate representation of specialized fields as well as supplying a better way of linking terms to concepts. The domain of coastal engineering is used as an example because the entities in play take part in processes that are difficult to describe only by means of conceptual trees. Through the use of corpus data we demonstrate how it is possible to represent such an event and create a dynamic frame which enriches and enhances the understanding of specialized field concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".