Bibliographic record
Abstract
In the widely bi- and multilingual context of work in terminology and terminography today, and with the increasing volume of text-based resources available for carrying out this work, there is a need for computer tools to assist terminologists and terminographers in their tasks — including knowledge acquisition, conceptual description, creation of concept systems, formulation of definitions, and establishment of equivalence between terms — in two or more languages. Conceptual relations may be very useful for all of these applications. One method of semi-automatically locating information about conceptual relations uses knowledge patterns, i.e., linguistic markers of relations that can be used to find segments of text that convey this information. However, this kind of approach faces a number of serious challenges, including that of evaluating the certainty and thus ultimate usefulness of the information identified. Moreover, to date little research has been carried out to evaluate whether knowledge-pattern-based approaches may be expected to encounter comparable phenomena with similar frequency in different languages. This article will explore expressions of uncertainty observed in occurrences of English and French knowledge patterns identifying conceptual relations of ASSOCIATION and CAUSE–EFFECT, and how these may differ in the two languages.
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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".