If it Matters, Measure it: Unpacking Diversification in Canada
Bibliographic record
Abstract
Will greater diversification benefit our economy? While many think it will, few are explicit about what they mean by diversification or what an “ideal” level would be. Even fewer recognize that favoured policies to promote diversification could actually do more harm than good. There are many ways to measure diversification in Canada, and each measure tells a different story. Canada’s GDP and employment, for example, are more diverse than many other countries, including the U.S. Employment is also more diversified today than at any point in its recent history, even in resource-rich provinces. Perhaps surprisingly, Alberta and Saskatchewan lead the country in employment diversity. Even accounting for non-resource jobs that are indirectly linked to resources does not reveal resource-rich provinces to be less diverse than others. To be sure, by other measures they are less diverse and more volatile, so we gather and analyze a wealth of data to paint a full, nuanced, and sometimes surprising picture of diversification in Canada. But does diversification even matter? Economists, for centuries, have found gains from specializing in areas where we have a comparative advantage. Subsidizing certain selected industries therefore risks causing economic damage by distorting activity and displacing workers and investment from more valuable uses. Policy-makers should therefore focus on neutral policies: create a favorable investment climate, facilitate adjustment and re-training, encourage savings (including by government), and so on. We discuss the pros and cons of various options. At the end of the day, responsible governments must define their objectives clearly, and recognize the costs of policies meant to achieve those objectives. We cannot hope to have a sensible debate on economic policy without full and complete information. If it matters, measure it.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".