Bibliographic record
Abstract
What age a state designates as appropriate for voting rights raises a range of democratic and empirical issues. The lowering of the voting age in Japan in 2015 was the biggest expansion of the country’s democratic franchise since 1945, yet it happened in an abrupt manner. Lowering the voting age was not a significant issue among the Japanese public until the mid-2000s and the government began supporting the move officially only in 2014. Why then? What happened to precipitate this decision? This study argues that the circumstances governing the period before the policy decision was made are crucial to understanding what followed. In the prevailing theories of policy change, analysis has focused much more on the phase of decision making over policy; public opinion, policy beliefs, and policy transfer have been prominently cited as the major reasons for lowering the voting age in other countries. In contrast, this article claims that the policy opportunity spillover, from constitutional revision to voting age, was a necessary condition for lowering the age. The discussion of constitutional revision incidentally opened a policy window to another issue area, in this case voting age. The findings help us answer the question of what time period we need to examine in order to discern actual policy dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".