Bibliographic record
Abstract
The purpose of a terminological definition is to represent in natural language the most relevant knowledge associated with a term. However, the knowledge activated by a term (i.e., its meaning) varies according to the usage context. Since context is indispensable in meaning construction, it should guide terminological definition writing. Nonetheless, the recommendation is still that a terminological definition should represent a concept's necessary and sufficient characteristics, which are regarded as context-independent. This paper proposes a parametrization of the contextual constraints applicable to terminological definitions so that context can be accounted for in them. To this end, the notions of premeaning and precontext are introduced, and different types of contextual constraints (linguistic, thematic, cultural, etc.) are discussed. We argue that the conscious application of contextual constraints by the terminologist helps to produce more useful definitions and to avoid inconsistencies and biases.
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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| 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".