Losers' Consent
Bibliographic record
Abstract
Abstract Democratic elections are designed to create unequal outcomes—for some to win, others have to lose. This book examines the consequences of this inequality for the legitimacy of democratic political institutions and systems. Using survey data collected in old and new democracies around the globe, the authors argue that losing generates ambivalent attitudes towards political authorities. Because the efficacy and ultimately the survival of democratic regimes can be seriously threatened if the losers do not consent to their loss, the central themes of this book focus on losing—how losers respond to their loss and how institutions shape losing. While there tends to be a gap in support for the political system between winners and losers, it is not ubiquitous. The book paints a picture of losers’ consent that portrays losers as political actors whose experience and whose incentives to accept defeat are shaped both by who they are as individuals as well as the political environment in which loss is given meaning. Given that the winner-loser gap in legitimacy is a persistent feature of democratic politics, the findings presented in this book have important implications for our understanding of the functioning and stability of democracies since being able to accept losing is one of the central, if not the central, requirement of democracy. The book contributes to our understanding of political legitimacy, comparative political behaviour, the comparative study of elections and political institutions, as well as issues of democratic stability, design, and transition.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".