Implications of the Recommendations of the Expert Panel on Federal Support to Research and Development
Bibliographic record
Abstract
Canada lags behind many of its First World counterparts when it comes to business innovation, and urgently needs to improve its performance if it is to remain competitive and attractive to investment. The Expert Panel Report on Federal Support to Research and Development has recommended several policy initiatives that governments need to enact to close the gap. This paper reviews all six major recommendations made by the Expert Panel and provides thorough assessments of each, with ample consideration given to their implications for the private sector. The two most promising are: (1) the consolidation of research and development spending programs at the federal level and (2) the adoption of smart procurement as a means of spurring innovation in the non-government sector. While some of the other recommendations need refinement and raise concerns about their impact on the economy, the message for government and business is clear: the former can and should facilitate Canadian business innovation by removing tax and regulatory burdens and facilitating better public-private cooperation, while the latter must make innovation a major part of corporate culture. This paper explains the consequences of the Panel’s recommendations for both sectors, identifies the deficiencies, and offers clear-eyed guidance for ameliorating them.
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 | 0.119 | 0.282 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.071 | 0.037 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".