Bibliographic record
Abstract
캐나다의 지역혁신지원정책의 핵심은, 연방정부는 전략적 연구분야와 목표에 대한 우선순위를 결정하고, 연구자로 하여금 연구네트워크를 형성하게 지원하는 것이다. 이 글에서는 캐나다 지역경제의 발전의 기반이 되는 분권적이고 시장지향적인 지역혁신지원체제 형성과 지역내/지역간 네트워킹형성의 특성을 분석하였다. 또한 캐나다 혁신클러스터의 특성인 학습(Learning), 노동력(Labour), 입지(Location), 리더십(Leadership), 공공부문의 역할(Legislation/ Labs)을 분석하였다. 마지막으로, 중앙집중적이고 시장기제에 적극적으로 개입하는 한국의 지역혁신지원체제의 개선방안에 대한 시사점을 도출하였다. 【The main thrusts of Canadian regional innovation policy lies in the two tract system. Federal government decides only the strategic research and development sectors and priorities, and then researchers and stockholders in the regions decide and implement the specific networking relationships and appropriate governance system. This paper reviewed the decentralized and market-friendly Canadian regional innovation support system and the characteristics of Canadian innovation clusters: Learning, Labour, Location, Leadership, Legislation/ Labs. finally, policy implications for Korean regional innovation system such as networking, formation of social capital, and business support systems are offered.】
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.038 |
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".