Implementation Intentions, Information, and Voter Turnout: An Experimental Study
Bibliographic record
Abstract
Are citizens more likely to vote when they are asked to make plans about how they will cast their ballots? Such planning—typically described as “implementation intentions”—has been shown to increase many types of desirable behaviors, including exercising and healthy eating, receiving vaccinations, physical rehabilitation, and recycling. Important earlier work in political science suggests voter turnout can also be influenced by implementation intention interventions, whereby electors are prompted to “make a plan” to vote (Nickerson & Rogers, ), though this finding has gone largely unreplicated. At the same time, elections management bodies (EMBs) in many contexts regularly conduct informational campaigns in the period leading up to elections, though little is known about the effects of such efforts upon turnout. Using data from an online experiment conducted at the time of the 2015 Canadian Federal Election, we demonstrate that implementation intention interventions can improve voter turnout but that this effect is conditional upon electors being exposed to informational materials about how to vote in the election. When survey respondents were provided with information on voting requirements and methods, and then prompted with questions forcing them to contemplate the act of casting their ballots, we observe a sizable increase in turnout rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".