Evaluating Academic Research Networks
Bibliographic record
Abstract
Abstract: Funding agencies and universities are increasingly searching for effective ways to support and strengthen a dynamic and competitive scientific research capacity. Many of their funding policies are based on the hypothesis that increased collaboration and networking between researchers and between institutions lead to improved scientific productivity. Although many studies have found positive correlations between academic collaborations and research performance, it is less clear how formal institutional networks contribute to this effect. Using social network analysis (SNA) methods, we highlight the distinction between what we define as “formal” institutional research networks and “organic” researcher networks. We also analyze the association between researchers’ actual structural position in such networks and their scientific performance. The data used come from curriculum vitae information of 125 researchers in two provincially funded research networks in Quebec, Canada. Our findings confirm a positive correlation between collaborations and research productivity. We also demonstrate that collaborations within the formal networks in our study constitute a relatively small component of the underlying organic network of collaborations. These findings contribute to the literature on evaluating policies and programs that pertain to institutional research networks and should stimulate research on the capacity of such networks to foster research productivity.
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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 | 0.028 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".