Bibliographic record
Abstract
The formation and current state of science policy in Canada are analyzed. Attention to this topic is explained by the fact that the country is a member of the G7 of the leading industrialized countries, although its population is only 0.5% and its GDP is about 2% of the global numbers. By international standards, Canada is not a leader in scientific and technological advance, its specificity being that, with relatively low R&D spending, it occupies leading positions in terms of indicators such as the number of scientific publications in international databases and the number of Nobel laureates (in the last 13 years alone, seven Canadian scientists have become Nobel prizewinners). Canadian affiliation makes up 3.6% of articles published in peer-reviewed journals worldwide. The evolution of the mechanisms of government support for science in Canada is traced, and current practices are summarized. The strengths and weaknesses of the Canadian model of organization of science are identified. This experience may be of interest to Russia.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.015 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".