Structure, Change over Time, and Outcomes of Research Collaboration Networks: The Case of GRAND
Bibliographic record
Abstract
In this dissertation, I study the interplay between the structure of a collaborative research network and the research outcomes produced by its members. To achieve this goal, I examine GRAND (an acronym for Graphics, Animation and New Media), a Canadian network with over 200 researchers funded by the Canadian government. I use the social network analysis (SNA) framework in order to aid in understanding the structure of the GRAND collaborative research network as well as how it changes over time. I look at the changes of the network's structural characteristics over time and how they interplay with the researchers' outcomes. Thereafter, I explain this interplay by discussing the changes of the structural network's characteristics as conditions that can potentially affect a researcher's social capital and that can, in turn, affect the researcher's outcomes. Using data collected through two online surveys, and research outcomes paper-based survey, I was able to capture the research networks (structure and changes over time) of GRAND researchers while also being able to obtain the perceptions of these individuals about their research outcomes. These networks captured four types of interaction among GRAND researchers: co-authorship of scholarly publications, communication activity, advice exchange, and interpersonal acquaintanceship. My sample consisted of 101 GRAND researchers, a subset of these researchers (N=50) were subsequently interviewed. My findings lend support to the argument that social capital and social networks, when combined, yield richer theory and better predictions than when used individually. The social networks analysis conducted in this research offer precise measures of the social structure and documents the changes in the GRAND research network. The social capital-driven findings help move beyond the relations themselves to understand how personal relationships or social structures can either facilitate or hinder the achievement of different research outcomes. These results offer to substantiate previous work, while drawing attention to the importance of analyzing interpersonal networks when studying factors effecting research outcomes. This direction for future study is especially relevant, as research collaboration continues to increase both in scope and in importance.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".