Bibliographic record
Abstract
Business innovation is key to a creating a highly productive Canadian economy. One concern is that Canadian businesses have relatively low levels of business research and development (R&D), despite access to some of the world’s most generous R&D tax subsidies. This doesn’t mean that tax credits do not stimulate R&D; indeed, research suggests they do. Rather, low business R&D appears to be rooted in structural aspects of the economy and, more importantly, a lack of demand-related pressure to pursue innovation as a business strategy. This paper looks at the impact of the federal R&D tax credit, and proposes tax options aimed at improving Canada’s innovation performance. It argues that Canada’s best bet is to focus on creating a competitive tax system across the entire innovation value chain. The current system is mainly designed to “push ” firms to undertake R&D through generous upfront subsidies. Meanwhile, the rewards of innovation—income from intellectual property and new products and services—are taxed at rates that are far less competitive. The federal government should focus its efforts on addressing market “pull ” factors by keeping taxes on the rewards of innovation at internationally competitive levels. It should also consider options to reduce tax disincentives for small, innovative firms to grow into larger, globally competitive companies.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.551 | 0.230 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".