Do winners pick government? How scale-up experience shapes entrepreneurs’ assessments of innovation policy mixes
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
Abstract How do entrepreneurs of high-growth firms in small, open economies evaluate innovation policy mixes? In response to market consolidation by large firms, governments in such countries are using a mix of innovation policy tools to support firms with high-growth potential in digitally intensive sectors. Government objectives, however, are not being realized. Bringing actor-centric perspectives to the policy mix literature, we analyze interviews with entrepreneurs from Canadian technology firms to determine whether there is a disconnect between the objectives and instruments employed by the government. With distinct policy preferences rooted in their growth experiences specific to the country’s political economy, we find that scale-up entrepreneurs prefer a more active role of the government in the form of demand-side, direct, and targeted innovation instruments. The findings presented in this article provide a more nuanced understanding of the innovation policy landscape and the preferences of technology scale-up firms
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it