Scientific Research and Experimental Development (SR&ED) Engagement and Management by Small Canadian-Controlled Private Corporations (CCPCs)
Bibliographic record
Abstract
Innovation plays a vital role in the growth of an economy.Spending on research and development (R&D) is an important determinant of the innovation process.Engaging in R&D can be considered risky for firms as there is no guarantee that there will be a positive return and concerns of competitors imitating a successful innovation.As a result, firms may be reluctant to engage in R&D.These challenges may be especially acute for small Canadiancontrolled private corporations (CCPCs), who may not have the same access to resources and expertise as larger firms.Given the importance of innovation for growth, governments seek to intervene to fix the R&D under-investment issue through grants, loans, or tax incentives.Since 1944, the Canadian government has used the Income Tax Act and R&D tax incentives to stimulate scientific research activity.The incentives have evolved, and since 1985, the primary mechanism used in Canada to encourage R&D is the Scientific Research and Experimental Development (SR&ED) program.The SR&ED program comprises tax deductions and an investment tax credit with differing rates for smaller CCPCs, individuals, and larger corporations.Traditional research in R&D and tax incentives has evaluated whether the incentives displace private R&D activities (crowding-out) or induce increased R&D expenditures and projects (additionality).There was little understanding of why CCPCs engage in R&D and how they manage their R&D practices.In order to better understand the motivation to engage in R&D x
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".