Polarizing information and support for reform
Bibliographic record
Abstract
We examine whether political polarization is an obstacle to common value reforms. We conduct experiments in two ideologically polarized countries, the United States and Egypt. Subjects vote between enacting a reform which yields higher expected financial payoffs than the costs of implementation for all (but has indirect differential benefits for supporters of only one group of voters) versus not enacting the reform and everyone receiving lower payoffs. We find that when the groups are polarized ideologically, subjects are less likely to vote for reform when informed that another political group would differentially benefit, and more likely to support reform should their own group benefit more. When subjects are told that one group will differentially benefit from reform, they are significantly more likely to explain their vote as being influenced by their own group membership. In contrast, we find that when subjects are organized into nonpolarized groups, group membership predicts reform support less, and when there are no differential benefits for a particular group, the effect of membership on support is significantly reduced. Hence, we find that the effect of polarization on support for common value reform is contingent on the existence of indirect differential benefits and the degree of ideological polarization of the groups who receive those benefits.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".