An Analysis of The Biomedical Sectors in Australia and \nCanada in a National Innovation Systems Context
Why this work is in the frame
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Bibliographic record
Abstract
The previous paper provided a comparison of the biomedical sectors in Australia and Canada based heavily on an analysis of biomedical alliances as well as a broader \nrange of indicators. The analysis suggested that the industry in Canada is much larger and more substantially integrated into the global biomedical industry than is simply explained by the relative size of the two countries. While there were significant issues of data definition in comparing the size of the industries in the two countries, the differences across the range of indicators are sufficient to suggest that the industry in Canada is 3 to 4 times the size of its Australian counterpart. This was supported by the data on alliances, which indicated that the degree of global integration of Canadian companies is substantially greater than is reflected in the differences of size between the two national industries. It showed significant differences in alliance patterns between the two countries. In particular Canadian biotechs had much greater success in forming high value later stage alliances with large pharma than Australian biotechs. In discussing a framework to explore the reasons for these differences it was agreed that a national innovation systems approach might be helpful. It would provide a framework within which to compare the essential features of the sectors in the two \ncountries and identify the role of policy in these differences. While the framework provided by national innovation systems has been used to research a range of industries, it is of particular relevance to R&D intensive sectors such as the biomedical industry (Lundvall 1992; Nelson 1988; Bartholomew 1997). This paper will firstly provide an outline of the major theoretical concepts discussed in the literature on national innovation systems, secondly review related empirical work on biomedical sector and finally begin to develop some explanations for the \ndifferences between Australia and Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.005 |
| 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