Cognitive support for political partisans’ understanding of policy data
Bibliographic record
Abstract
Political partisanship might lead educated adults-even the highly numerate-to reason selectively about numbers that are relevant to and support their ideology ("motivated numeracy"). In this pre-registered study, we sought to examine the replicability of motivated numeracy, and investigate whether cognitive support (number lines) that improves the reasoning of children might also improve the reasoning of political partisans. To test this, we asked 1000 adults about their political ideology and asked them to interpret fictional data, in a table or number-line format, about ideology relevant (i.e., the effect of gun control on crime) or irrelevant (i.e., the effect of skin cream on rash) issues. We failed to replicate motivated numeracy when political identity was used but observed motivated numeracy when prior attitude was used. Moreover, data presented on number lines elicited 75 percent greater accuracy than data presented in tables, regardless of whether the information was ideology-relevant, or whether data supported, was neutral to, or contradicted participants' political outlooks. Findings imply that political partisans require cognitive support to be more objective about policy data.
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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 | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".