How Does Diversity Impact Innovation in Research Network? A Multilevel Study of the GRAND NCE
Bibliographic record
Abstract
In this dissertation, I study the interplay between network structure and group characteristics of diversity as well as the motivation of members in a collaborative research network and the innovative 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 develop a framework which combines multilevel models and social network analysis, in order to understand how memberships in multiple projects, disciplinary diversity in research projects, members' motivation to participate in multidisciplinary collaborations (MDCs), and prior collaborations influence GRAND members' cross-disciplinary collaborations as well as creation of innovation. I conducted qualitative analysis to explain the complexity of MDCs by discussing the difficulties brought about by disciplinary diversity, the disciplinary differences in MDC norms, researchers' perceptions of disciplinary boundaries, and the various types of MDC activities. The findings show that the network structure and multidisciplinary culture emphasized by GRAND indeed foster intellectual interactions cross disciplinary boundaries. However, disciplinary diversity does not directly lead to innovation. Instead, its effect is mediated by diversity in researchers' ego networks. Furthermore, motivation also has a strong effect on diversity in ego networks rather than on innovative outcomes. In other words, diversity in interaction rather than diversity in composition creates innovation, and the former type of diversity can be increased by a high level of motivation and membership in multiple projects. This study has confirmed the strengths of network form of organizations. The findings suggest that allowing members to be involved in multiple work units provides them with more opportunities to interact with their colleagues from various disciplines and thus, produce innovative outcomes.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".