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    <titleInfo>
        <title>Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England</title>
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    <name type="personal">
        <namePart type="given">Amanda</namePart>
        <namePart type="family">Cole</namePart>
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            <title>Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022</title>
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        <name type="personal">
            <namePart type="given">Chris</namePart>
            <namePart type="family">Callison-Burch</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Christopher</namePart>
            <namePart type="family">Cieri</namePart>
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        <name type="personal">
            <namePart type="given">James</namePart>
            <namePart type="family">Fiumara</namePart>
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        <name type="personal">
            <namePart type="given">Mark</namePart>
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    <abstract>Five participants, each located in distinct locations (USA, Canada, South Africa, Scotland and (South East) England), identified the self-determined social class of a corpus of 227 speakers (born 1986–2001; from South East England) based on 10-second passage readings. This pilot study demonstrates the potential for using crowdsourcing to collect sociolinguistic data, specifically using LanguageARC, especially when geographic spread of participants is desirable but not easily possible using traditional fieldwork methods. Results show that, firstly, accuracy at identifying social class is relatively low when compared to other factors, including when the same speech stimuli were used (e.g., ethnicity: Cole 2020). Secondly, participants identified speakers’ social class significantly better than chance for a three-class distinction (working, middle, upper) but not for a six-class distinction. Thirdly, despite some differences in performance, the participant located in South East England did not perform significantly better than other participants, suggesting that the participant’s presumed greater familiarity with sociolinguistic variation in the region may not have been advantageous. Finally, there is a distinction to be made between participants’ ability to pinpoint a speaker’s exact social class membership and their ability to identify the speaker’s relative class position. This paper discusses the role of social identification tasks in illuminating how speech is categorised and interpreted.</abstract>
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            <start>38</start>
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%0 Conference Proceedings
%T Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England
%A Cole, Amanda
%Y Callison-Burch, Chris
%Y Cieri, Christopher
%Y Fiumara, James
%Y Liberman, Mark
%S Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F cole-2022-crowdsourced
%X Five participants, each located in distinct locations (USA, Canada, South Africa, Scotland and (South East) England), identified the self-determined social class of a corpus of 227 speakers (born 1986–2001; from South East England) based on 10-second passage readings. This pilot study demonstrates the potential for using crowdsourcing to collect sociolinguistic data, specifically using LanguageARC, especially when geographic spread of participants is desirable but not easily possible using traditional fieldwork methods. Results show that, firstly, accuracy at identifying social class is relatively low when compared to other factors, including when the same speech stimuli were used (e.g., ethnicity: Cole 2020). Secondly, participants identified speakers’ social class significantly better than chance for a three-class distinction (working, middle, upper) but not for a six-class distinction. Thirdly, despite some differences in performance, the participant located in South East England did not perform significantly better than other participants, suggesting that the participant’s presumed greater familiarity with sociolinguistic variation in the region may not have been advantageous. Finally, there is a distinction to be made between participants’ ability to pinpoint a speaker’s exact social class membership and their ability to identify the speaker’s relative class position. This paper discusses the role of social identification tasks in illuminating how speech is categorised and interpreted.
%U https://aclanthology.org/2022.nidcp-1.7
%P 38-45

Markdown (Informal)

[Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England](https://aclanthology.org/2022.nidcp-1.7) (Cole, NIDCP 2022)

• Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England (Cole, NIDCP 2022)

ACL

• Amanda Cole. 2022. Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England. In Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022, pages 38–45, Marseille, France. European Language Resources Association.