Offshoring and Outsourcing of R&D and Business Activities in Canadian Technology Firms
Bibliographic record
Abstract
A substantial amount of empirical research has been conducted on the offshoring/outsourcing practices of U.S., European, South Korean, and Japanese technology firms. However, there is very little research evidence on the strategies of Canadian firms. This leaves a gap in the literature that we aim to fulfill by providing empirical evidence of the practice among Canadian manufacturing firms. The evidence presented is based on t he 2009Survey of Innovation and Business Strategy conducted by Statistics Canada. This survey provides the largest and most comprehensive data in Canada on this topic. The data suggest that only a very small proportion of Canadian manufacturing firms offshore/outsource their R&D and other business activities and only a select few countries. The primary motivations for Canadian firms to offshore/outsource their R&D and business activities are to reduce costs and to gain access to new markets. The managerial, policy, and research implications of the results are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".