Visualization and evolution of the scientific structure of fuzzy sets research in Spain
Bibliographic record
Abstract
Introduction. Presents the first bibliometric study on the evolution of the fuzzy sets theory field. It is specially focused on the research carried out by the Spanish comunity. \nMethod. The CoPalRed software, for network analysis, and the co-word analysis technique are used. \nAnalysis. Bibliometric maps showing the main associations among the main concepts in the field are provided for the periods 1965-1993, 1994-1998, 1999-2003 and 2004-2008. \nResults. The bibliometric maps obtained provide insight into the structure of the fuzzy sets theory research in the Spanish community, visualize the research subfields, and show the existing relationships between those subfields. Furthermore, we compare the Spanish community with other countries (the USA and Canada; the UK and Germany; and Japan and Peoples Republic of China). \nConclusions. As a result of the analysis, a complete study of the evolution of the Spanish fuzzy sets community and an analysis of its international importance are presented.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".