Evidence map›Paper›PMID 38941314›Full record

ArticlePloS one2024

The racialization of pit bulls: What dogs can teach us about racial politics.

Michael Tesler, Mary McThomas

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael TeslerUniversity of California, Irvine, California, United States of America.ORCID 0000-0002-9875-5772
Mary McThomasUniversity of California, Irvine, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many have argued that discrimination against pit bulls is rooted in the breed's association with Black owners and culture. We theoretically and empirically interrogate that argument in a variety of ways and uncover striking similarities between the racialization of pit bulls and other racialized issues (e.g., poverty and crime) in public opinion and policy implementation. After detailing the reasons to expect pit bulls to be racialized as Black despite dog ownership in the U.S. generally being raced as white, the article shows: (1) Most Americans associate pit bulls with Black people. (2) Anti-Black attitudes, in general, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them; (3) stereotypes of Black men as violent, in particular, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them. (4) Implicit racialization through a national survey experiment further eroded support for legalizing pits, with the treatment effect significantly conditioned by respondent's race. And (5) state-level racial prejudice is a significant negative predictor of enacting legislation to preempt breed-specific bans. We conclude with our findings' broader insights into the nature of U.S. racial politics. Michael Tesler, mtesler@uci.edu, corresponding author, is Professor of Political Science at UC Irvine; Mary McThomas, mary.mcthomas@uci.edu, is Associate Professor of Political Science at UC Irvine. An earlier version of this paper was presented at the American Political Science Association's annual meeting. We thank Maneesh Arora, Rachel Bernhard, Nathan Chan, Louis Pickett, David Sears, DeSipio, Adam Duberstein, Jane Junn, Claire Kim, Jessica Manforti, J. Scott Matthews, Justin.

Indexed as

DogsPetsPoliticsRacismAnimalsAttitudeBlack or African AmericanFemaleHumansMalePublic OpinionUnited StatesWhite

Identifiers

PMID38941314
PMCPMC11213322

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.