Bibliographic record
Abstract
정책학습에 관한 연구가 걸어온 발자취를 간단히 더듬어보고 정책학습의 개념과 유사개념을 검토한 다음 여러 사람의 견해에 따라 정책학습의 유형을 살펴보았다. 이어서 정책학습과정과 정책변동에 들어가 갈등모형과 학습모형의 관계를 검토한 다음 Hall의 모델과 May의 견해에 따라 정책학습과정과 정책변동을 살펴보았다. 끝으로 정책학습에 기인한 정책변동 사례로서 캐나다의 원주민 정책변동, 알래스카해안 오염방지정책, 한국의 지방양여금제도 도입의 3개 사례를 고찰하고 정책학습의 성격, 정책학습의 용이성, 정책변동의 비중이라는 세 가지 기준에 따라 3개 사례를 비교하여 보았다. This paper briefly traces how the studies on policy learning developed and then examines the concept and typologies of policy learning. This study also examines the policy learning process which results in policy change. The concepts developled here are examined through three case studies, Lessons from the old and new Canadian policies towards aboriginal peoples, The EXXON Valdez oil spill in Prince William Sound, Alaska, and The adoption of the shared tax system between central and local governments in Korea.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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; both teacher heads agree on what is shown here.
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".