Political Scientists and Electoral Reforms in Europe and Canada: What They Know, What They Do
Bibliographic record
Abstract
In 2013, the American Political Science Association (APSA) Task Force on Political Science, Electoral Rules, and Democratic Governance released a report in which the authors say that more than 50 U.S.-based political scientists have been involved in electoral reform processes in the U.S. and abroad since 2010 (Htun and Powell 2013). In this symposium, we give new insights to this topic by offering a view from the outside of the U.S. European and Canadian political scientists who had been invited to give their opinion about the electoral system of their country, and who engaged with politicians, public officials, and national media on the topic, talk about their experience as national experts. They answer two related questions: (1) What do political scientists know about electoral reform that practitioners do not? and (2) Does it make a difference? This symposium gathers their respective contributions to the roundtable. In this introduction, we give a brief overview of the literature on the role of political scientists in electoral reforms and summarize the main conclusions of the four contributions to the symposium.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.030 | 0.032 |
| Scholarly communication | 0.037 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| 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".