Panorama intelectual de la terminología a través del análisis de redes sociales
Bibliographic record
Abstract
When transmitting scientific knowledge, authors weave a web of intellectual affinities through the works they cite that portrays trends and developments in research in their discipline. In the present article, we aim to establish an intellectual panorama of Terminology in which we depict the outstanding developments in research and the most influential authors. To do so, we analyze periodical publications that have appeared over a wide period of time. Author Citation Analysis (ACA) and the visual representation of the relationships between authors through social networks (specifically, pathfinder networks) is based on the premise that links are necessarily established between the authors cited in any specific work so that greater frequency of co-ocurrence indicates a stronger affinity between authors. Among other findings, our results show that the group of most frequently cited authors represents less than 1% of the total and that only 12% of authors have published three or more articles. Moreover, we can confirm that research in Terminology is developing in three clearly differentiated directions: theoretical foundations, Natural Language Processing and Socioterminology.
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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".