Contributions of bibliometrics to the study of interdiscipline. A methodology for the analysis of the intersection between the fields of neurosciences and computational sciences
Bibliographic record
Abstract
Despite the growing importance of interdisciplinary studies for the development of science, quantitative works on the subject are not abundant. Bibliometrics offers tools to analyze interdisciplinarity through a complementary approach to qualitative work. While there is a body of precedents in bibliometrics (1,2,3,4,5,6), methodological proposals for the construction of databases of the intersection of two disciplines are scarce.(7) Thus, a proposal is made to identify an interdisciplinary field with a set of scholarly articles. The objective of this work is to develop a methodology for defining the intersection between the fields of neuroscience and computational science. This area of study is not directly traceable from categorizations in databases. For this reason, three strategies are built to delimit an interdisciplinary corpus and compare the potential and limitations of each of them. The three strategies are focused, on the one hand, on keywords and, on the other hand, on citation and reference patterns using the Web Of Science database. It is found that it is possible to operationalize the interdiscipline with two types of approaches: 1. A semantic approach based on the use of keywords. A relational approach focusing on cross-references and citations between articles from the two disciplines. As a result, a basis for the study of the intersection between the fields of neurosciences and computational sciences from a bibliometric perspective is obtained, and a methodological proposal for the quantitative study of interdiscipline in other areas of knowledge is mad
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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 | 0.046 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.152 | 0.189 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".