Contributions of bibliometrics to the study of interdiscipline. A methodology for the analysis of the intersection between the fields of neurosciences and computational sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.082 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it