The Governance of Social Economy and Regional Development in Korea
Bibliographic record
Abstract
21세기를 전후로 글로벌경제화의 심화, 산업구조의 변화, 저출산·고령사회 등 급속한 환경변화로 지역사회의 공동화와 지속가능성의 위기가 심화되고 있다. 이에 대한 대안으로 다양한 지역의 맥락과 특성에 기반을 둔 사회적 경제의 영역의 활성화를 통한 지역발전 전략이 전 세계적으로 주목을 받고 있다. 이러한 맥락에서 이 연구는 다음과 같은 주제에 대하여 연구하고자 한다. 첫째, 국가마다 맥락의 차이를 인정하는 전제 하에 사회적 경제를 위한 효과적인 지역거버넌스의 요소를 파악하고자 한다. 여기에서는 특히 사회적 경제의 협력적 거버넌스의 성공 사례 중 캐나다 퀘벡 주에 초점을 맞추어 분석틀을 제시하고자 한다. 둘째, 이런 맥락에서 비교적 퀘벡주의 모델을 따라하면서도 자신들의 지역적 맥락에 따라 사회적 경제의 지역거버넌스의 형성에 비교적 잘 진행되고 있다고 여겨지는 서울시 성북구와 충청남도 홍성군을 비교분석하고자 한다. 이 두 지역은 도시와 농촌이라는 또 서로 다른 맥락 속에서 사회적 경제 형성의 독특함과 어려움을 보여주고 있다. 셋째, 이를 바탕으로 한국의 사회적 경제 활성화를 위하여 지역거버넌스를 구축 방안에 대하여 제시하고자 한다.Since the 21st century, the rapid environmental changes such as globalization, changing the industrial structure, and aging society are hollowing out the local economy, and its sustainability is becoming critical in the future. It has been widely recognized that the social economy sector can play an essential role in the local development in the face of these rapid environmental changes. Especially the partnership governance between the local government and the social economy sector can be more effective in transforming the economy and politics for the local development. The best example of this governance is the Quebec experience since the late 1990s. This paper examines the core principles of the Quebec model and attempts to apply it to some Korean cases where similar development patterns are occurring such as Seongbuk-gu in Seoul Metropolitan City and Hong Seong-gun in the Chungnam Province to find out some similarities and differences and construct a useful model fit to the Korean context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".