New paths to knowledge, innovation and development: Canadian leadership in emerging global partnerships
Bibliographic record
Abstract
The establishment of various policy experiments such as the Canada Foundation for Innovation, the Canada Research Chairs, Genome Canada, and the Canadian Institutes of Health Research, not to mention increased funding for university research and the National Research Council, have succeeded in creating a knowledge renaissance both within the research community and through concomitant partnerships with the private sector, governments, and the higher education sectors. Knowledge is a universal currency, and innovation a global enterprise. Attention needs to be paid to building on Canada's legacy as a knowledge broker, and its future as a global leader in innovation, in order to maintain and improve its status as a leading economy for trade, investment, development and R&D. Discovery, skills and innovation know no boundaries in today's wired and inter‐connected world, and Canada is confronted with challenges from new and emerging knowledge producers. However, Canada has no strategic focus in place to meet these challenges or to tackle new opportunities in the global knowledge market. In short, Canadian decisionmakers and knowledge leaders have to think “intermestically”. Domestic and international decisions are two sides of the same coin.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".