André Blais and Jean-François Daoust. <i>The Motivation to Vote: Explaining Electoral Participation</i>
Bibliographic record
Abstract
These authors center on the “paradox of voting.” Blais and Daoust, along with many other political scientists, try to ascertain the motivation to vote, when the personal benefits and the costs of voting would lead a rational person to abstain from voting. They have created a superior primer on voter turnout that succinctly and expertly captures previous research and also provides additional significant research. Blais and Daoust focus on the individual-level attitudinal determinants of turnout by examining two basic predispositions (interest in politics and civic duty) and two election-specific assessments (care and ease of voting), and the interaction among these four attitudes. For most of this analysis, Blais and Daoust use the Making Electoral Democracy Work (MEDW) surveys, conducted in two regions in five countries: Canada, France, Spain, Switzerland, and Germany. These surveys, conducted between 2011 and 2015, cover a total of twenty-four elections. Following the introductory chapter, in chapter 2, Blais and Daoust provide an excellent, thorough analysis and summary of their previous research, and that of others, on the association between voter turnout, on the one hand, and age, race, gender, education, and income.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".