Evaluating the social capital accrued in large research networks: The case of the Sustainable Forest Management Network (1995-2009)
Bibliographic record
Abstract
This paper examines the social capital that evolved in the Sustainable Forest Management Network (SFMN), one of the Canadian Networks of Centres of Excellence. Our longitudinal study shows a sevenfold increase in the total number of researchers and a high density of relationships among (researchers from) provinces across the country. The results of a social network analysis revealed that 52.6 percent of the network researchers maintained the same number of collaborators while 46.7 percent increased their number of collaborators enormously: the maximum increase in number of collaborators being 6900 percent and the minimum 6 percent. A bibliometric analysis suggested that the number of publications was strongly correlated to measures of social capital. From a science and innovation policy perspective, the finding that more than half of the researchers in the SFMN did not increase their personal networks of collaborators raises important questions. A theoretical model is proposed to examine whether funding agencies should focus on fostering various network structures and evolutions or rely on competition in the distribution of research funds through networks. The proposed model is designed to measure the impact of various network structures on the development of social capital and research output.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".