The Biomedical Sectors in Australia and Canada: A Comparative Analysis
Bibliographic record
Abstract
As countries, Canada and Australia have much in common. There is a shared heritage as new world British colonies and accordingly a similar culture, governmental institutions, living conditions, health and educational standards. Australia is somewhat smaller than Canada - its population of about 20m is 61% of Canada's. Both have high living standards although Canada's GDP per capita is marginally higher than Australia's. The countries also share many aspirations. One is to retain their technological edge, as innovative societies, through the commercialisation of their science base. Little could illustrate this better than the release, within the space of a few months, of innovation strategies designed to enhance the innovation process in each country. In Australia's case, its plan was set out in Backing Australia's Ability (DEST 2001), which followed a number of related reports and white papers, and for Canada, the more substantial document Achieving Excellence (Government of Canada 2002). This provided not only a detailed analysis and assessment of Canada's innovation performance, but also identified quantifiable targets to guide future action by government and industry. Both documents focussed on similar things, strengthening R&D, accelerating its commercial application and developing and retaining skills. They also emphasised the importance of broader supportive and competitive economic settings. In both cases, the governments' policy initiatives were accompanied by substantial increases in government funding for R&D and associated support programs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.029 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".