RESEARCH AND DEVELOPMENT INVESTMENT EXPENDITURES IN CANADA’S NATURAL RESOURCES SECTOR 1
Bibliographic record
Abstract
The paper examines R&D investment expenditures within the context of Canada’s natural resources sector. Although R&D in the sector is below the economy’s average, the sector is considered a leader in innovation; it undertakes significant capital investment and surpasses other sectors of the economy in terms of productivity. The natural resources sector is considered a leader in the development and adoption of new innovative techniques. It spends more than $44B annually on capital investments (almost 25 % of Canada’s total), more than any other sector in Canada. In explaining the R&D estimates, the paper argues that R&D expenditures do not reflect the level of innovation and productivity in the sector. The paper draws on the literature in the public domain, highlights key results on how R&D takes place in the natural resources sector from the Statistics Canada 1999 Innovation Survey, and cites recent work on productivity by the Centre of the Study of Living Standards (2004) undertaken for NRCan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".