Maintaining the balance between knowledge and the lexicon in terminology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper argues for an approach to terms—based on Frame Semantics (Fillmore in Ann N Y Acad Sci Conf Origin Dev Lang Speech 280:20–32, 1976; Fillmore and Baker in A Frames Approach to Semantic Analysis, 313–339, 2010)— that takes into account their linguistic properties and shows how terms and their properties are connected formally to the expression of knowledge in specialized fields. I briefly present the theoretical assumptions underlying this proposal. The main part of the article describes the methodology devised to implement the proposal in two terminological resources that are under development at the Observatoire de linguistique Sens-Texte (OLST). The methodology that comprises seven main steps is based on that of FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/, 2017. Accessed 20 January 2017) (Ruppenhofer et al. in FrameNet II: extended theory and practice. https://framenet.icsi.berkeley.edu/fndrupal/index.21php?q=the_book, 2016. Accessed 27 January 2017), the lexical implementation of Frame Semantics. I illustrate the methodology by applying it to terms that belong to the field of endangered species, a subfield of the environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it