Innovation policy in resource-rich economies
Bibliographic record
Abstract
Innovation is considered as a major driver of long-run economic growth with recent work showing that at least 50 per cent of growth is directly attributable to it. Innovation involves much more than changes to technology. It involves linkages, interactions and influences of many kinds between firms, universities, research centres, and government. Effective innovation depends on all such connections being in place and working well. The way all these work together to influence the development and utilisation of new knowledge and learning defines a country’s innovation system. Some aspects of innovation systems are national, others regional and sectoral or local. \n \nThis report examines innovation policy and performance in eight jurisdictions — the Canadian provinces of Alberta and British Columbia, South Africa, Chile, Brazil, and the Scandinavian countries of Finland, Norway and Sweden. The analysis highlights countries’ strengths and weaknesses in innovation as well as the effectiveness of their innovation systems and policies in driving economic performance. It aims to create an understanding of how countries act to develop and improve their capability for innovation. The report also compares policies and identifies commonalities and good practice in the sample countries with the view of identifying policy options for strengthening Western Australia’s innovation capacity.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".