Bibliographic record
Abstract
The federal budget contains some sensible changes to the SR&ED investment tax credit, but the decision to reduce support for large firms to provide additional support for small firms is a step in the wrong direction. The Jenkins Panel expressed concern about excessive subsidization of small and medium-sized firms and recommended cutting back on the enhanced SR&ED credit in order to finance more targeted support for these firms. Following that advice would have improved the social return on support for R&D; in contrast, the budget measures marginally reduce the benefits to society from subsidizing R&D. The budget also announced $400 million in additional funding for risk capital. Returns in the venture capital industry are very low and the additional funding is unlikely to be successfully deployed until returns improve. There is abundant evidence that the tax credit for investment in Labour-Sponsored Venture Capital Corporations is crowding out private investment and contributing to low rates of return; eliminating the credit is therefore an essential first step in restoring the financial health of the venture capital industry.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".