Bibliographic record
Abstract
The future of the western industrialized economies, including Canada, depends on healthy and innovative high-tech sectors. In 2010, this realization spurred the Canadian government to commission a blue-ribbon panel charged with assessing the state of programs designed to support business and commercially oriented research and development. The resultant Jenkins Report contains many useful recommendations aimed at consolidating disparate offerings, measuring existing initiatives’ performance and fostering federal-provincial cooperation to improve programs’ impact on the tech sector. However, the Report erred in overlooking the squandering of government resources on tax subsidies to investors in Labour-Sponsored Venture Capital Corporations (LSVCCs) — union-sponsored specialized mutual funds meant to promote the development of high-growth small and mediums-sized businesses — which are far outperformed by the private sector and often waste capital better used elsewhere. It is also unduly harsh on the federal Scientific Research and Experimental Development Tax Credit, which is critical to the early-stage start-ups that give rise to high-tech giants. In judiciously assessing the Jenkins Report’s recommendations and offering alternatives, this paper serves as a much-needed corrective, offering policy makers clear guidance in securing Canada’s economic future.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".