Performance of public-private collaborations in advanced technology research networks : network analyses of Genome Canada projects
Bibliographic record
Abstract
Globalisation and the quest for competitiveness in a global market represents a new era of connectedness within public-private networks of experts in an effort to pursue research objectives in advanced technology industries. Balancing the competing interests of public good and private gain, reducing the barriers in terms of access to knowledge and intellectual property and ensuring that efforts result in socially valuable outcomes in the form of new innovations can be difficult, to say the least. Although widely advocated and implemented, collaborations have not, as yet, been fully examined nor have appropriate performance evaluation models been developed to evaluate them. This dissertation hypothesizes that a history of social relationships or collaborative activity amongst network actors is positively correlated with high performance in networks. Incorporating descriptive statistics with the social network analysis tool, this dissertation proposes and tests a novel framework and compares two distinct Genome Canada funded research networks. Other factors explored are the roles of proximity, institution and research focus in characterizing network structure and in affecting performance.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".