A longitudinal comparative study of the role of entrepreneurship in research commercialisation performance: Australia, UK and USA
Bibliographic record
Abstract
Research commercialisation surveys are now available fm Australia for the FY 2000 and 2002. The paper reviews longitudinal comparative data for research commercialisation performance in Australia, the United Kingdom, Canada and the USA. Commercialisation performance measures with a specific focus on entrepreneurial spin-off companies are discussed and performance ,comparisons have been made based on research expenditure in US dollars adjusted for purchasing power parity. The conclusions from these analyses suggest that Australian public agency performance in generation of spin-offs, that is New Technology Small Firms, per unit of research expenditure adjusted for purchasing power parity has been comparable in recent years to and in some sectors superior to that in the other countries reviewed. However, Australian university revenue from intellectual property licensing royalties and research contracts has been below that of the other countries studied. The analysis suggests that this results from both a demand side problem , low business investment in R&D and hence low technology absorptive capacity, and a supply side problem, that is lack of time and lack of incentive to academic researchers to develop contacts with and meet the expectations of industry and other research users for technology that works.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".