Harvesting State Support: Institutional Change and Local Agency in Japanese Agriculture
Bibliographic record
Abstract
Once established, institutions function stably over the long term. However, institutions do not remain entirely unchanged. Rather, they often change gradually and incrementally over time. This slow institutional change can affect the way people behave and lead to explicit political and economic changes. Gradual institutional change is likely to occur when there are actors who strongly resist reform in a rigid political system (Zakowski 2020). Therefore, in a country like Japan, where the Liberal Democratic Party (LDP) has many interest groups as supporting organizations and has been in power for a long period of time, gradual institutional change is easier to analyse. The post-war Japanese agricultural support and protection regime was based on food self-sufficiency and consisted of a scrum of LDP norin zoku-giin (legislators who represented farmers’ interests), the Ministry of Agriculture, Forestry and Fisheries (MAFF), the agricultural cooperatives (nogyo kyodo kumiai, Japan agriculture cooperatives: JA), and farmers. However, since the 1990s, over the course of trade liberalization negotiations, the Japanese government, under strong external pressure to open up its agricultural sector, has gradually reformed the core institutions of the regime, despite resistance from the agricultural sector (Yoshida 2012). This book examines this Japanese agricultural support and protection regime from the perspective of gradual institutional 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".