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
The spectacular success of several well-known new ventures in technological fields, which in little more than a decade have jumped from the state of start-ups to that of top international businesses, has pointed to innovation as a key factor in the high growth of firms. These high-growth enterprises often drive job creation and innovation, so policy makers are increasingly making such companies a key focus. Specifically, how can government policy foster the creation of more high-growth enterprises; what are the growth factors, and how can they be leveraged; what are the appropriate ways to provide such support? To help answer these questions, this report presents findings from two new research studies: (1) reports from 15 countries (Australia, Brazil, Canada, Chile, Czech Republic, Finland, France, Italy, Japan, Mexico, Netherlands, Portugal, Spain, Switzerland and Tunisia) that provide interesting insights into the operations of and challenges faced by high-growth enterprises; (2) a policy survey by the OECD Working Party on SMEs and Entrepreneurship, which reviewed more than 340 programmes that policy makers in 24 countries have put in place to support the growth of enterprises. Some of this reportâs findings may surprise: any firm can be a growth company; growth is almost always a temporary phase; high-growth small firms are funded mostly by debt, not equity. These and many more insights are summarised and analysed, providing policy makers with ideas on how to power growth at the firm level.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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