Evaluating the social capital accrued in large research networks: The case of the Sustainable Forest Management Network (1995-2009)
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
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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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 | 0.174 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.186 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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