Bibliographic record
Abstract
Movements to encourage civic participation and cooperation are being adopted by local governments across Japan . This paper explores the forces that execute these policies and the responses from local governments to these forces. This paper applies urban regime analysis to evaluate three policies for civic participation and cooperation in Chiba City. Since 1950, the city was governed by a “development oriented” regime, but an administration change in 2009 altered the orientation of government policy. During the time of the development-oriented regime, four different mayors were supported by the conservative party, businesses, and labor unions, which offered a stable alliance for many years. However, in 2009, the new mayor, Kumagai Toshihito, who was supported by many unorganized citizens as well as left-center parties, tried to increase general citizens' participation in Chiba's urban regime. This led to Kumagai adding many unorganized citizens to his regime. The analysis of these three policies showed that efforts toward civic participation and cooperation are progressing in the Kumagai administration, which is consistent with national trends toward increasing government participation. Using urban regime analysis, this paper suggests that progress by civic participation and cooperation policies is associated with an expanded number of actors involved in a local governmental regime and that some regimes are better able to attract citizen participants than others.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".