Bibliographic record
Abstract
The recent report of the Council of Canadian Academies (2018) on the state of science, technology and industrial R&D in Canada provides an authoritative assessment of where Canada stands relative to our major competitors. The CCA report reveals that Canada remains strong in several fields of research; the recent Nobel awards in widely different domains of physics to Arthur B. McDonald ( 2015) and Donna Strickland ( 2018) and the granting of the A.M. Turing Award to Canadian computer scientists Yoshua Bengio (2019) and Geoffrey Hinton (Toronto, 2019), provide ample support for this assessment. More generally, Canadian scientists are highly regarded; their average citation rank is above the world average in all fields and Canada stands in fourth place in terms of research reputation. Of particular concern, however, is that compared to other OECD countries.
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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.030 | 0.048 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.037 | 0.026 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".