Bibliographic record
Abstract
Inclusive innovation requires that opportunities for participation in innovation be broadly available and that the benefits of innovation be broadly shared. This report considers a number of innovation policy reforms through the lens of this dual emphasis. For policies that would facilitate both innovation and inclusiveness, there is a strong case for implementation. Policies that might promote innovation at the expense of inclusiveness would require that the trade-off be managed or mitigated. Education and training is a potential area of complementarity between inclusiveness and innovation because a highly skilled population is an important facilitator of both. Clusters pose a potential trade-off between the goals of innovation and inclusion, which must be taken into account in the context of policies aimed at supporting their development. There is no strong case for subsidizing small businesses generally. Instead, targeted support should be provided to help growth-oriented small firms to grow. The scope for further regulatory improvement to enhance innovation may be limited, given what Canada has already done in recent decades. But room for improvement still exists in terms of foreign investment barriers and the speediness and accessibility of the legal system. Government investment can play a productive role in an inclusive innovation system; the government should increase direct funding for basic research, especially in clean energy technology.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".