The role of federal government funding on the outreach programs of independent industrial R&D establishments in Canada
Bibliographic record
Abstract
Purpose Properly nurtured and financially supported independent industrial R&D institutions (IRDIs) can play a pivotal role in converting knowledge into commercially exploitable applications in manufacturing industries particularly in the small and medium ones. The purpose of this paper is to present various evidence to enhance government awareness that Canadian R&D funding agenda should be streamlined as the way to strengthen the outreach capacity of Canadian IRDIs. Design/methodology/approach Using a variety of evidence, the position and role of IRDIs in Germany, Japan, the USA and Canada are highlighted. This is done to reveal the current position and outreach of IRDIs in each country and through that to recommend helpful strategies to strengthen the Canadian IRDIs and foster their contribution to the manufacturing technology development. Findings The study revealed the weak position of Canadian IRDIs in comparison with their counterparts in the USA, Japan and Germany. The paper proposed strategies and approaches on how IRDIs should be financially and technically supported to expand their outreach in the Canadian manufacturing sector. Research limitations/implications This paper provides secondary data‐based evidence intended to serve as a background for more focused case supported future research. Practical implications Stakeholders at both government and industrial sectors may find the recommendations given in the paper as helpful inputs for formulating suitable policies and strategies in this area. Originality/value The paper presents vital background information on the important but neglected role of IRDIs in the application and commercialization of knowledge in manufacturing technology and the need to strengthen their position by granting the necessary financial assistance.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".