R&D expenditures by field of science and GDP: Which causes which in Canada?
Bibliographic record
Abstract
This paper attempts to reveal the relationship between GDP per capita and R&D expenditure per capita, R&D expenditure per capita on natural sciences and engineering, and R&D expenditure per capita on social sciences and humanities for Canada. Based on data from 1981 to 2014, bootstrap causality test proposed by Hacker and Hatemi-J (2006) show that there is a unidirectional causality from GDP per capita to R&D expenditure per capita, and a unidirectional causality from GDP per capita to R&D expenditure per capita on natural sciences and engineering. However, no causal relationship is observed between R&D expenditure per capita on social sciences and humanities and GDP per capita. These results may point an indirect relationship between the variables or the validity of R&D paradox and the European paradox for Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".