The effect of interdisciplinary components' citation intensity on scientific impact
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
Purpose This study explores whether interdisciplinary components' citation intensity (ICCI) affects papers' scientific impact. In this study, the term “interdisciplinary components” refers to the disciplines that are different from the discipline to which the target research belongs. The citation intensity is the degree of density or sparseness of the paper citation network for a discipline. Previous studies have shown that the scientific impact of interdisciplinary research is influenced by interdisciplinarity and its properties, namely, variety, balance and disparity. However, the effect of ICCI on scientific impact has not been comprehensively explored. Design/methodology/approach This study is based on the entire publication database of the Web of Science for the year 2000, where the authors provide an indicator to measure the ICCI of each publication. A tobit regression model is used to examine the effect of ICCI on scientific impact, controlling for a range of variables associated with the characteristics of the publications studied. Findings The results show that ICCI has a positive effect on scientific impact. The authors’ results further point out that ICCI displays a curvilinear inverted U-shape relationship with scientific impact. It means that including more citation-intensive interdisciplinary components can increase the scientific impact of interdisciplinary research. However, excessive use of citation-intensive interdisciplinary components may reduce the scientific impact of interdisciplinary research. Originality/value This study shows that, in addition to interdisciplinarity, the scientific impact of interdisciplinary research is also affected by the citation characteristics of interdisciplinary components, namely ICCI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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