R&D centres of excellence in Canada
Bibliographic record
Abstract
This chapter examines CoEs in the context of the R&D operations of foreign subsidiaries in Canada. Its aim is to provide some background on the literature on foreign-owned R&D operations in Canada, and then to provide evidence from the authors’ current research on this topic. To some extent the ‘centre of excellence’ terminology is a case of old wine in new bottles, in that there has been a steady stream of research over the past thirty years concerned with understanding the R&D activities of foreign firms in Canada. However, the CoE terminology does also add something, in that it helps to clarify the role of R&D centres within their corporate system, and the conditions under which they are able to deliver on that role. The guiding research questions for this chapter are two. What is the evidence for R&D CoEs in foreign-owned subsidiary companies in Canada? And what are the factors that are associated with their existence? The chapter is in two parts. The first part draws on the existing literature—both literature concerned with the development of CoEs in multinational firms and the literature that looks specifically at R&D in Canada. The second part is written on the basis of our recent empirical research in this area. Specifically, it reports on a survey of 99 foreign-owned subsidiaries in Canada, and on a series of case-study interviews conducted by the authors over the last five years.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".