Examining Communities in the Transdisciplinary Area of Cognitive Science: Automatic Classification for Examining Communities in the Web of Science Using Unsupervised Clustering Methods
Bibliographic record
Abstract
We propose methodology for examining classification to identify and make explicit community perspectives that are neglected by traditional journal-subject classification in order to provide a more flexible and customizable classification system. Our method is based on keyword matches, and is applied to the broad transdisciplinary area of cognitive science. In the Web of Science (WoS), Scopus, and the National Science Foundation (NSF) classification, the classification of journals places each journal into a silo based on pre-determined categories deemed appropriate to demonstrate the relatedness of journals. Classification at the journal level does not necessarily represent the perspectives of a community, as a community in both membership and topical scope may transcend the bounds of a single journal classification. Our approach is novel because we examine topics within the transdisciplinary domain of cognitive science, and within that domain, we identify community perspectives on the conceptual contents as found in the titles of publications in the WoS.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".