What’s a term? An attempt to define the term within the theoretical framework of text linguistics
Bibliographic record
Abstract
In texts for specific purposes, terms adopt a behaviour which is contrary to the prescriptive demands of traditional terminology. Indeed, they exhibit variability both on the level of their meaning content and on the level of their linear structure. Their meaning contents are not fixed, but may be changed by the language user’s verbal and non-verbal activities. Their linear structures are not fixed, but can be adjusted to the cha racteristics of their linguistic environment, specifically the sentence or sequence of sentences in which they are being used. Examined within the framework of text linguistics, it becomes clear that this variability con- tributes to two basic characteristics of any body of sentences which constitutes a text, namely text coherence and text cohesion. Consequently, the aim of this article is to propose a new definition of the term, a definition which underscores the role the term plays in bringing about texture in texts for specific purposes.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".