scholarly journals Predictors of accent-based prejudice

2021 ◽  
Author(s):  
David Matthew Sumantry
Keyword(s):  
Latin America ◽  
Theoretical Models ◽  
Mechanical Turk ◽  
Right Wing ◽  
Negative Stereotypes ◽  
Suitable Alternative ◽  
Content Model ◽  
Stereotype Content ◽  
Amazon's Mechanical Turk ◽  
Direct Measures

This thesis investigated accent-based stereotyping and prejudice – a line of research originating in Lambert et al. (1960) – by studying perceptions of four accented groups. Participants recruited from Amazon’s Mechanical Turk listened to audio clips where the speakers had native accents from either Toronto, Latin America, Arabic countries, or India. They then evaluated the speakers on several dimensions based on the Stereotype Content Model (SCM) and the solidarity-status-dynamism model (SSD), and completed direct measures of prejudice. Speakers were not evaluated differently on measures of prejudice but were stereotyped differently. Participants higher in right-wing ideologies held more negative stereotypes of speakers and demonstrated greater prejudice. Comparing theoretical models indicated that the more commonly-used SCM provides a suitable alternative to the SSD model. Implications for research on accent-based prejudice are discussed.

scholarly journals Predictors of accent-based prejudice

2021 ◽  
Author(s):  
David Matthew Sumantry
Keyword(s):  
Latin America ◽  
Theoretical Models ◽  
Mechanical Turk ◽  
Right Wing ◽  
Negative Stereotypes ◽  
Suitable Alternative ◽  
Content Model ◽  
Stereotype Content ◽  
Amazon's Mechanical Turk ◽  
Direct Measures

This thesis investigated accent-based stereotyping and prejudice – a line of research originating in Lambert et al. (1960) – by studying perceptions of four accented groups. Participants recruited from Amazon’s Mechanical Turk listened to audio clips where the speakers had native accents from either Toronto, Latin America, Arabic countries, or India. They then evaluated the speakers on several dimensions based on the Stereotype Content Model (SCM) and the solidarity-status-dynamism model (SSD), and completed direct measures of prejudice. Speakers were not evaluated differently on measures of prejudice but were stereotyped differently. Participants higher in right-wing ideologies held more negative stereotypes of speakers and demonstrated greater prejudice. Comparing theoretical models indicated that the more commonly-used SCM provides a suitable alternative to the SSD model. Implications for research on accent-based prejudice are discussed.

The Stereotype Content of Sexual Orientation

2018 ◽  
Vol 5 (5) ◽  
pp. 458-478 ◽  
Author(s):  
Trenton D. Mize ◽  
Bianca Manago
Keyword(s):  
Sexual Orientation ◽  
Gay Men ◽  
Minority Groups ◽  
Social Groups ◽  
Bisexual Men ◽  
Lesbian Women ◽  
Negative Stereotypes ◽  
Content Model ◽  
Stereotype Content ◽  
Orientation Group

The stereotype content model provides a powerful tool to examine influential societal stereotypes associated with social groups. We theorize how stereotypes of gender, sexuality, and a group’s status in society combine to influence societal views of sexual orientation groups—placing particular emphasis on stereotypes of warmth and competence. In two survey experiments, we collect quantitative measures of stereotype content and open-response items on the stereotypes of bisexual individuals. We predict—and find—that gay men and lesbian women face disadvantaging stereotypes; bisexual men and women, however, face the most severely negative stereotypes of any sexual orientation group—with aggregate judgments of low warmth and competence. In the second study, using a diverse sample, we show that stereotypes about sexual orientation groups are largely culturally consensual. We conclude by emphasizing the importance of comparative approaches that consider both advantaged and disadvantaged groups to fully contextualize stereotypes of minority groups.

Animals as Social Objects

2016 ◽  
Vol 21 (3) ◽  
pp. 206-217 ◽  
Author(s):  
Verónica Sevillano ◽  
Susan T. Fiske
Keyword(s):  
Social Perception ◽  
Animal Species ◽  
Social Groups ◽  
Social Responses ◽  
Content Model ◽  
Stereotype Content ◽  
Nonhuman Animals ◽  
Behavioral Tendencies ◽  
Similarities And Differences ◽  
Sphere Of Influence

Abstract. Nonhuman animals are typically excluded from the scope of social psychology. This article presents animals as social objects – targets of human social responses – overviewing the similarities and differences with human targets. The focus here is on perceiving animal species as social groups. Reflecting the two fundamental dimensions of humans’ social cognition – perceived warmth (benign or ill intent) and competence (high or low ability), proposed within the Stereotype Content Model ( Fiske, Cuddy, Glick, & Xu, 2002 ) – animal stereotypes are identified, together with associated prejudices and behavioral tendencies. In line with human intergroup threats, both realistic and symbolic threats associated with animals are reviewed. As a whole, animals appear to be social perception targets within the human sphere of influence and a valid topic for research.

Stereotypes of Social Groups in Germany in Terms of Warmth and Competence

2010 ◽  
Vol 41 (2) ◽  
pp. 76-81 ◽  
Author(s):  
Frank Asbrock
Keyword(s):  
Gay Men ◽  
Social Groups ◽  
Social Judgment ◽  
Cultural Stereotypes ◽  
Student Sample ◽  
Content Model ◽  
Stereotype Content ◽  
German Student ◽  
Stable Clusters ◽  
Relevant Social Groups

The stereotype content model says that warmth and competence are fundamental dimensions of social judgment. This brief report analyzes the cultural stereotypes of relevant social groups in a German student sample (N = 82). In support of the model, stereotypes of 29 societal groups led to five stable clusters of differing warmth and competence evaluations. As expected, clusters cover all four possible combinations of warmth and competence. The study also reports unique findings for the German context, for example, similarities between the perceptions of Turks and other foreigners. Moreover, it points to different stereotypes of lesbians and gay men.

A Technical Guide to Using Amazon's Mechanical Turk in Behavioral Accounting Research

2017 ◽  
Vol 30 (1) ◽  
pp. 111-122 ◽  
Author(s):  
Steve Buchheit ◽  
Marcus M. Doxey ◽  
Troy Pollard ◽  
Shane R. Stinson
Keyword(s):  
Third Party ◽  
Mechanical Turk ◽  
Behavioral Accounting ◽  
Online Data ◽  
Research Participants ◽  
Traditional Research ◽  
Potential Benefits ◽  
Technical Guide ◽  
Online Data Collection ◽  
Amazon's Mechanical Turk

ABSTRACT Multiple social science researchers claim that online data collection, mainly via Amazon's Mechanical Turk (MTurk), has revolutionized the behavioral sciences (Gureckis et al. 2016; Litman, Robinson, and Abberbock 2017). While MTurk-based research has grown exponentially in recent years (Chandler and Shapiro 2016), reasonable concerns have been raised about online research participants' ability to proxy for traditional research participants (Chandler, Mueller, and Paolacci 2014). This paper reviews recent MTurk research and provides further guidance for recruiting samples of MTurk participants from populations of interest to behavioral accounting researchers. First, we provide guidance on the logistics of using MTurk and discuss the potential benefits offered by TurkPrime, a third-party service provider. Second, we discuss ways to overcome challenges related to targeted participant recruiting in an online environment. Finally, we offer suggestions for disclosures that authors may provide about their efforts to attract participants and analyze responses.

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