The language of discrimination: assessing attention discrimination by Hungarian local governments.
In our study we assess the responsiveness of Hungarian local governments to requests for information by Roma and non-Roma clients, relying on a nationwide correspondence study. Our paper has both methodological and substantive relevance. The methodological novelty is that we treat discrimination as...
| Publicado en: | Language Resources & Evaluation Vol. 57; no. 4; pp. 1547 - 1571 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=173723387&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173723387 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2023 vid: 57 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 173723387 10.1007/s10579-022-09612-5 ppf: 1547 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 941KB tig: atl: The language of discrimination: assessing attention discrimination by Hungarian local governments. aug: au: Buda, Jakab Németh, Renáta Simonovits, Bori Simonovits, Gábor affil: https://ror.org/01jsq2704 Research Center for Computational Social Science, Faculty of Social Sciences, Eötvös Loránd University, Pázmány Péter sétány 1/a, 1117, Budapest, Hungary https://ror.org/01jsq2704 Faculty of Education and Psychology, Eötvös Loránd University, Izabella utca 46, 1064, Budapest, Hungary Associate Professor, Department of Political Science, Central European University, Budapest, Hungary Co-director for Academic Affairs, Rajk Laszlo College for Advanced Studies, Budapest, Hungary https://ror.org/04vr0gs97 Senior Researcher, Institute for Political Science, Budapest, Hungary su: Discriminatory language Local government Natural language processing Machine learning Government information sug: subj: Discriminatory language Local government Natural language processing Machine learning Government information keyword: Attention discrimination Controlled field experiment Correspondence study Metric of discrimination ab: In our study we assess the responsiveness of Hungarian local governments to requests for information by Roma and non-Roma clients, relying on a nationwide correspondence study. Our paper has both methodological and substantive relevance. The methodological novelty is that we treat discrimination as a classification problem and study to what extent emails written to Roma and non-Roma clients can be distinguished, which in turn serves as a metric of discrimination in general. We show that it is possible to detect discrimination in textual data in an automated way without human coding, and that machine learning (ML) may detect features of discrimination that human coders may not recognize. To the best of our knowledge, our study is the first attempt to assess discrimination using ML techniques. From a substantive point of view, our study focuses on linguistic features the algorithm detects behind the discrimination. Our models worked significantly better compared to random classification (the accuracy of the best of our models was 61%), confirming the differential treatment of Roma clients. The most important predictors showed that the answers sent to ostensibly Roma clients are not only shorter, but their tone is less polite and more reserved, supporting the idea of attention discrimination, in line with the results of Bartos et al. (2016). A higher level of attention discrimination is detectable against male senders, and in smaller settlements. Also, our results can be interpreted as digital discrimination in the sense in which Edelman and Luca (2014) use this term. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2023. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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