A Novel Indicator for Measuring Science-technology Linkage Based on Paper-patent Co-cited
Bibliographic record
Abstract
This paper proposes a novel method for measuring the linkage between science and technology (S&T) based on the co-citation of patents and academic papers.This method differs significantly from the Science Linkage (SL) index, a science-based index first proposed by Narin.By analyzing the co-citation of patents and papers in references, we can detect the relationship of S&T.This method uses the co-citation strength of papers and patents in references to characterize the strength of S&T linkage.The citations of patent databases in some countries and regions are incomplete, making it difficult to calculate the relevance of S&T by the SL method based on direct citation.However, the method based on patent-paper co-citation proposed in this paper is effective.The author also applied the approach to patent-citing documents registered between 2001 and 2015 from the United States, Germany, France, Australia, South Korea, and Canada (hereinafter referred to as the five countries) and checked the performance.Results showed that the proposed approach and the indirect S&T linkages (indSL), a new indicator, are valuable for detecting the relationship of S&T.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".