Canadian Productivity Growth, Secular Stagnation, and Technological Change
Bibliographic record
Abstract
In this article, we show first that the recent slowdown in productivity growth in Canada, similar to that in the United States, can be attributed at least in part to the fall-off in the commercialization of new technologies. Using our bookbased indicators of technological change, we are able to show that this is true for both aggregate measures of technology and, at the disaggregate level, for mechanical/manufacturing and electrical technologies. Our results also indicate that the productivity impact of the slowdown in Canada is much greater on goods-producing industries than it is on services. Second, our latest results suggest that, contrary to the concerns of some that we are entering a new period of secular stagnation characterized by low productivity and economic growth, we are actually on the threshold of significant new technological breakthroughs, associated largely, but not only, with advances in artificial intelligence (AI) and robotics. Provided that Canadian firms adopt these innovations, we can anticipate not a continuation of slow productivity growth but an acceleration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".