Bibliographic record
Abstract
The stunning real GDP per capita increase experienced by Canadians since Confederation has enabled us to enjoy one of the highest standards of living in the world. 1 Two features of this growth are noteworthy.First, it could have turned out differently.At Confederation, Canada was economically on a par with Argentina, but the two countries took very different approaches to policy and governance.Argentina embraced protectionism and restrictive regulations, and favoured state enterprises over private ones, all of which contributed to Argentina's failure to launch.Second, while many factors have played a role in Canada's remarkable growth record, technological change stands out as critical.Innovation has enabled us to boost the quantity and the quality of capital goods and has made us much more productive at transforming inputs, such as labour and machine time, into outputs.The close of Canada's sesquicentennial presents an opportunity to pinpoint the policies that allowed us to become a technological juggernaut, to draw on them to help us fashion ones that will aid us in meeting the challenges of the future, and to insure that the gains from new innovation are widely shared by all Canadians. technological Change and Policy: the Views and Lessons from PastDuring the second industrial revolution (approx.1867-1918) many countries experienced waves of major technological advance, including breakthroughs in steel production, energy production, telecommunications, transportation, chemistry, and agriculture.During these years, innovations nourished one another and it became clear to scientists, inventors, and policymakers that scientific advances were the bedrock of new technologies.In Canada, this realization led to the 1916 establishment of the Canadian National Research Council, among other initiatives.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".