Examining social networks between educational institutions, industrial partners, and the Canadian government
Bibliographic record
Abstract
Between 2000 and 2010, the Canadian federal government disbursed $716 893 740 in research grants through the Natural Sciences and Engineering Research Council of Canada (NSERC)'s Research Partnerships Programs. This is only one branch, of one agency, in the immense bureaucracy of government funding. This study will examine the social networks which resulted from these research grants. Over this eleven years period, 6112 individual grants were awarded through these programs, involving 187 educational institutions and 4231 industrial partners. We used social network analysis (SNA) to explore these networks. We employed centrality measures to identify key entities in the networks and visualization tools to provide a “big-picture” view of the network topology. Specifically, this study will examine changing trends in the leading educational institutions, industrial partners, and areas of research over the last decade. While the limited scope of our study can only manage to scratch the surface of such a large network, we aim to provide a broad over-view of the problem and, hopefully, to demonstrate the importance of, and to inspire, future explorations in this complex field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".