INCORPORATING NETWORK ANALYSIS INTO EVALUATION OF 'BIG SCIENCE' PROJECTS: AN ASSESSMENT OF THE CANADIAN LIGHT SOURCE SYNCHROTRON
Bibliographic record
Abstract
Major investments in science and technology are designed to generate something beyond the science itself. Government-funded big science infrastructure, exemplified by the Canadian Light Source Synchrotron (CLS), offers places for scientists both to conduct their scientific investigations and to do things that more directly add economic and social value. Scientists, however, do not work in isolation. They are usually part of larger networks or communities that can generate bigger net effects for the affiliated individuals and institutions. Understanding the structure and scale of these scientific networks provides insights into the impacts of big science on the scientific community (locally and globally) and the potential opportunities that may be realised. This study applies the social network analysis (SNA) methodology and combines it with a survey and statistical analysis to assess the network of scholars attached to the CLS, to explore the evolution of collaborative behaviour over time and to explore the relationships between the network and specific output and outcome variables. The study concludes that the CLS has generated a large and growing scholarly community; at the core of the network is a group of highly linked and engaged scholars who have the aptitude and experience to extend their research results into application and use.
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How this classification was reachedexpand
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 | 0.111 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.056 | 0.154 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".