Does Misinformation Demobilize the Electorate? Measuring the Impact of Alleged 'Robocalls' in the 2011 Canadian Election
Bibliographic record
Abstract
The paper presents evidence on the effect of voter demobilization in the context of the Canadian 2011 federal election. Voters in 27 ridings (as of February 26, 2012) allegedly received automated phone calls (`robocalls') that either contained misleading information about the location of their polling station, or were harassing in nature, claiming to originate from a particular candidate in the contest for local Member of Parliament. We use within-riding variation in turnout and vote--share for each party to study how turnout changed from the 2008 to the 2011 election as a function of the predominant party affiliation of voters at a particular polling station. We show that those polling stations with predominantly non-conservative voters experienced a decline in voter turnout from 2008 to 2011, and that this effect was larger in ridings that were allegedly targeted by the fraudulent phone calls. The results thus indicate a statistically significant effect of the alleged demobilization efforts: in those ridings where allegations of robocalls emerged, turnout was an estimated 3 percentage points lower on average. This reduction in turnout translates into roughly 2, 500 eligible (registered) voters that did not go to the polls. The 95%-confidence interval gives a lower bound estimate of 1, 000 fewer votes cast in robocall ridings, which is still a sizable effect.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".