Boosting productivity through greater small business dynamism in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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 it