Empathy and Political Preferences (Study 1)
Bibliographic record
Abstract
Individuals differ in their ability to experience the emotions of others. Upon seeing an individual in a moment of emotional turmoil, some individuals will be able to experience that same tumult. Likewise, some individuals are able to experience the fear, sadness, happiness, or joy of others. People vary in their ability to put themselves into the shoes of others- sharing their feelings and understanding their thoughts. In this project, we aim to demonstrate that this ability, empathy, also matters for politics (e.g., Allamong and Peterson 2021; Feldman et al. 2020; Harell, Hincley, and Mansell 2021). We argue that empathy exercises an influence over political preferences in a consistent and theoretically informed way. To this extent, the authors produced an unpublished manuscript using data collected between 2007 and 2014, Loewen, Cochrane, and Arsenault (2017) presenting findings indicating that individuals who have a greater empathic capacity are more likely to identify with parties of the left and are more likely to indicate support for new spending programs, even at a personal cost. This pre-registration aims to replicate the findings from Loewen, Cochrane, and Arsenault (2017) in the United States and Canada (study 1). For more information, please see PAP_Empathy_and_Political_Preferences.pdf.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".