Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 744

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Race and Gender Cues in Low-Information Elections

Mcdermott, Monika L·1998·Political Research Quarterly

Abstract

Previous research has demonstrated that in low-information elections voters compensate for a lack of information by taking informational short-cuts, based on candidate cues, to make voting decisions. To date, this research has focused on candidate party identification and incumbency cues. This article argues that candidate demographic cues, specifically race and gender, also play an influential role. Unlike past psychological research that focuses on potential voter bias against women or black candidates, this article examines the informational content of voters' stereotypes about women and black candidates, and how these stereotypes affect voting behavior. I use quasi-experimental data from the Los Angeles Times Poll to demonstrate that candidate gender and candidate race signal voters in two different ways. Voters stereotype candidates ideologically: women and black candidates are stereotyped as more liberal than the average white male. Voters also stereotype candidates on issues: black candidates are seen as more concerned with minority rights than whites; while women candidates are viewed as more dedicated to honest government. As a result, voters choose candidates for office based on how much they agree or disagree with the ideological and issue positions they attribute, through stereotyping, to candidates.

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 · 10.1177/106591299805100403

Cites

2 of 2 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

Cites

Show all 2 references

Cited by

Metadata

Authors
Mcdermott, M. L.
Venue
Political Research Quarterly
Published
1998-12
Volume
51
Issue
4
Pages
895-918
ISSN
  • 1065-9129 (ISSN-L)
  • 1938-274X
OpenAlex
W2136750123
Cited by
744
References
2
FWCI
35.29
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: Race and Gender Cues in Low-Information Elections. https://app.cochat.ai/papers/10.1177/106591299805100403
The paper
Mcdermott, M. L. (1998). Race and Gender Cues in Low-Information Elections. Political Research Quarterly. https://doi.org/10.1177/106591299805100403

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.