Economic Efficiency of Federal Provision of Scientific Information and Support for Science Research in Canada
Bibliographic record
Abstract
Federal provision of scientific information and support for science research have declined significantly in recent years, and a trend away from pure research toward applied and commercializable research has begun. This commentary asks whether those and other trends in this area can be justified in terms of economic efficiency. It is argued that the marginal benefits of scientific information and research have likely risen relative to production costs and the marginal cost of public funds has fallen, implying that reduced provision cannot be justified on efficiency grounds. An efficiency rationale for increased support for commercializable research is also lacking. Longer-term trends include the rise of open data, which does make sense, and a relative shift from intramural government research and development toward university-based R&D. The latter shift likely responds to underlying economic factors, but does not warrant a sudden sharp reduction in government research, such as seen recently in Canada.
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".