Invalid Votes, Deliberate Abstentions, and the Brazilian Crisis of Representation
Bibliographic record
Abstract
Abstract August 31, 2016 registered the historical impeachment of Brazilian president, Dilma Rousseff, indicted for contravening the budget law and misstating the public deficit that propelled the country into deep economic recession. Many disagreements on this matter have permeated the country’s conflict atmosphere, supported by arguments that the collective will of more than 54 million voters was disrespected. Based on 3,010 interviews conducted in 204 Brazilian cities, we construct a pairwise comparison to present arguments that Rousseff had no legitimate representation in the 2014 national elections. We demonstrate how the suboptimal support of invalid votes and deliberate abstentions might have misrepresented the results of Brazilian presidential election by choosing a pseudo‐Condorcet loser candidate. The results in the Brazilian case study presented here point to the weakness in the social process of aggregating preferences by relative or absolute majority, and sets out recommendations. Related Articles Galatas, Steven. 2008. “‘None of the Above?’ Casting Blank Ballots in Ontario Provincial Elections.” Politics & Policy 36 (3): 448‐473. https://doi.org/10.1111/j.1747-1346.2008.00116.x Stockemer, Daniel. 2013. “Corruption and Turnout in Presidential Elections: A Macro‐Level Quantitative Analysis.” Politics & Policy 41 (2): 189‐212. https://doi.org/10.1111/polp.12012 Stockemer, Daniel, and Stephanie Parent. 2014. “The Inequality Turnout Nexus: New Evidence from Presidential Elections.” Politics & Policy 42 (2): 221‐245. https://doi.org/10.1111/polp.12067 Related Media The Conversation. 2017. “Kenneth Arrow’s Legacy and Why Elections Can Be Flawed.” March 1. https://theconversation.com/kenneth-arrows-legacy-and-why-elections-can-be-flawed-73675
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".