Educational Administration: Theory and Practice
Bibliographic record
Abstract
The concept of "One Nation, One Election" has garnered significant attention and debate in recent years, especially within the framework of federal democracies. This research paper conducts a comprehensive comparative analysis of the global experiences surrounding the implementation or discussion of synchronized elections in countries with federal systems of governance. The primary objective of this study is to examine the practicality, challenges, and implications of harmonizing electoral cycles at various levels of government in federal democracies. It delves into the political, constitutional, and logistical considerations that influence the decision to pursue such electoral reforms. By focusing on a diverse set of countries, including India, the United States, Canada, Australia, Germany, and South Africa, this research aims to provide a nuanced understanding of the concept's feasibility and impact in distinct federal contexts. The paper explores the historical development and current status of discussions or implementations related to "One Nation, One Election" in each country, taking into account the unique features of their federal systems. It assesses the potential benefits of synchronized elections, such as cost reduction and improved policy continuity, while also considering the potential drawbacks and concerns related to voter fatigue, constitutional constraints, and political strategies.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".