Bibliographic record
Abstract
A proposal for segmentation and characterisation of the terminographical process, based on Gouadec’s model for translation, is presented in this paper. It comes as a result of a systematisation and comparative analysis of the various existing characterisations of the terminographical process, in an attempt – that has proven to be unfruitful – to identify the one, or a combination of different ones, which would meet the specific needs to build a resource targeted at a non-specialist public. The terminographical process is thus organised in three phases ( pre-terminography , terminography , and post-terminography ), and comprises three dimensions of analysis ( conceptual , communicative , and textual ). Broadly speaking, in pre-terminography , a preparatory piece of research is carried out (special subject field familiarisation, communicative contexts identification, and building of specialized corpora), a step which is essential to the next phase ( terminography ), in which a terminological database is built and populated. The last phase ( post - terminography ) comprises efforts aimed at the industrial application of the resource, as well as underscoring the need for its continual update. This methodology also takes into account the three dimensions of terms (conceptual, communicative and textual) which are applied and adopted in the process of termbase creation itself.
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 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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".