Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 239

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Partisan Bias and the Bayesian Ideal in the Study of Public Opinion

Bullock, John G.·2009·The Journal of Politics

Abstract

Bayes’ Theorem is increasingly used as a benchmark against which to judge the quality of citizens’ thinking, but some of its implications are not well understood. A common claim is that Bayesians must agree more as they learn and that the failure of partisans to do the same is evidence of bias in their responses to new information. Formal inspection of Bayesian learning models shows that this is a misunderstanding. Learning need not create agreement among Bayesians. Disagreement among partisans is never clear evidence of bias. And although most partisans are not Bayesians, their reactions to new information are surprisingly consistent with the ideal of Bayesian rationality.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1017/s0022381609090914

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 239

Metadata

Authors
Bullock, J. G.
Venue
The Journal of Politics
Published
2009-07
Volume
71
Issue
3
Pages
1109-1124
ISSN
  • 0022-3816 (ISSN-L)
  • 1468-2508
OpenAlex
W2011572126
Cited by
239
References
3
FWCI
42.58
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Partisan Bias and the Bayesian Ideal in the Study of Public Opinion. https://app.cochat.ai/papers/10.1017/s0022381609090914
The paper
Bullock, J. G. (2009). Partisan Bias and the Bayesian Ideal in the Study of Public Opinion. The Journal of Politics. https://doi.org/10.1017/s0022381609090914

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.