Nazita Lajevardi’s: Outsiders at Home: The Politics of American Islamophobia
Bibliographic record
Abstract
This paper aims to understand the structure of actors involved in the increase of the number of lawyers in Korea. For this research, the Canadian Scholar’s Policy Network Approach and the Sabatier’s Advocacy Coalition Concept were used. The case analysis focuses on the policy decisions related to the number of people passing the bar examination. This paper reviewed the policy debates, initiatives and actions of actors concerned before and during the 1995 legal reform process. The research result is as follows: The Supreme Court, the prosecution and the national bar association have been a very powerful and very organized policy community affecting the issues mentioned. As a sub-government, they have played a key role in deciding the number of lawyers in the country. They have effectively prevented the issues affecting their interests from being policy agenda. However, the law professors’ associations and civic groups remained as an attentive public. A policy network highly represented by the lawyers’ community, which appears to be clientele oriented, existed for a long time. However, during the reform, the structure of the policy network changed a lot. As the presidential office initiated the reform, the attentive public participated in the core policy making process. In relation to this, the network changed from clientele oriented to a cooperative one. In addition, there was policy learning between the groups. Policy makers must realize that it is necessary to collaborate with the attentive public and at the same time change the structure of the policy network. This is to modify the policy which has been unchanged because of the existence of the strong policy community. This paper also proves that the irregular policy making process, such as reform, could be more effective and that policy learning is very important for the policy change.
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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 | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".