Strengthening ties: The influence of microtargeting on partisan attitudes and the vote
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the resources devoted to microtargeting in recent election campaigns, we still have a limited understanding of its impacts on the electorate. This article aims to test the reinforcement effect of microtargeted messages on voters’ attitudes. Specifically, it looks at how microtargeting influences the strength and stability of partisan affiliation and the probability of voters changing their vote choice during the 2015 Canadian election campaign. Given that individuals are not targeted randomly, entropy balancing is used to model selection into treatment and create a valid counterfactual for microtargeted individuals. This approach is complemented by an extensive sensitivity analysis to improve confidence in selection on observables. We find evidence that microtargeting reinforces party ties and makes voters less likely to defect from their preferred party.
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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 | 0.000 | 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 it