Boosting productivity through greater small business dynamism in Canada
Bibliographic record
Abstract
Small business dynamism is a feature of an SME sector that contributes to overall productivity growth, not an end in itself. Such dynamism increases productivity growth by reallocating resources towards more productive firms and strengthening the diffusion of new technologies. Small business dynamism in Canada has declined in recent decades, as in other OECD countries, but overall it remains in the middle of the range, with some indicators above average and others below. Framework economic policies are generally supportive of small business dynamism, especially labour regulation, but there is scope to reduce regulatory barriers to product market competition. Canada has many programmes to support small businesses. Some of the largest programmes are not well focused on reducing market failures. Focusing support more on reducing clear market failures would increase the contribution of these programmes to productivity growth and living standards. This would likely entail redirecting support from small businesses in general to start-ups and young firms with innovative projects, which would boost small business dynamism. This Working Paper relates to the 2016 OECD Economic Survey of Canada (www.oecd.org/eco/surveys/economic-survey-canada.htm)
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".