Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology
Bibliographic record
Abstract
We address the question of whether or not geographical, cognitive, and collaborative proximity have an impact on citation probability in the scientific writings of Canadian nanotechnology. Even though a number of studies in the proximity literature deal with the effects of spatial distance, scientific specialization, and social network structure, to our knowledge no one has combined all three to explore the production of academic information. We generate a feature framework based on measurements from these factors, relying on statistical and classification approaches to assess their influence on effective citations. Specifically, by means of applying binary regression models along with tree-based machine learning algorithms, we found statistical significance proving that these features have both a verifiable impact and predictive potential. Importantly, our work is the first one that we have seen combining these techniques to infer the establishment of positive citation links. Moreover, we employed inductive network analysis comprehensively to examine the co-authorship links between authors publishing in nanoscience, considering additional network metrics to the ones usually adopted in the literature. Our findings reveal that cognitive proximity, closely followed by the collaborative aspect, are the most important elements inducing Canadian scholars to cite, with geography sometimes acting as their base. Our results enable us to reach better understanding related to the citation behavior of the nanoresearch community in Canada, making our work a valuable contribution to scholarly literature, also giving us ground to make policy recommendations.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".