Bibliographic record
Abstract
Canada has a prosperity problem because we don t create enough wealth. Business as usual is not a solution; we need to create more value in new ways - that s innovation. But beyond innovation to solve our current problems, we must learn how to innovate in new ways to deal with whatever future pressures and opportunities arise from global demographics and climate change. Innovation in Canada demystifies innovation and presents its many aspects in one big picture. The book proposes innovation in both goods and services as the means for increasing the value of what the Canadian economy produces. This will raise our prosperity and show up as improved productivity. Written in plain language and illustrated with corporate data, the book underlines the essential roles of technology, entrepreneurship and commerce. It points out important differences between innovation in established firms and innovation in new ventures, whose time scales are shorter and whose needs are more urgent. Innovation in Canada proposes the elements of a supportive government innovation policy, and it outlines the different design principles for government assistance programs needed to provide effective support to the two different groups of innovative 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 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".