Abstract
Inclusion, as one of the foundations in the diversity, equity, and inclusion initiative, concerns the degree of being treated as an ingroup member in a workplace. Despite of its importance in a corporate’s ecosystem, the inclusion strategies and its performance are not adequately addressed in corporate social responsibility (CSR) and CSR reporting. This study proposes a machine learning and big data-based model to examine inclusion through the use of stereotype content in actual language use. The distribution of the stereotype content in general corpora of a given society is utilized as a baseline, with which texts about corporate texts are compared. This study not only propose a model to identify and classify inclusion in language use, but also provides insights to measure and track progress by including inclusion in CSR reports as a strategy to build an inclusive corporate team.
Original language | English |
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Title of host publication | Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference |
Editors | Mingyu Wan, Chu-Ren Huang |
Publisher | European Language Resources Association (ELRA) |
Pages | 46–51 |
ISBN (Electronic) | 9791095546894 |
Publication status | Published - Jun 2022 |
Event | Language Resources and Evaluation Conference 2022 - Palais du Pharo , France Duration: 20 Jun 2022 → 25 Jun 2022 https://lrec2022.lrec-conf.org/en/ |
Conference
Conference | Language Resources and Evaluation Conference 2022 |
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Abbreviated title | LREC |
Country/Territory | France |
Period | 20/06/22 → 25/06/22 |
Internet address |
Keywords
- corporate social responsibility (CSR) reports
- inclusion
- word embeddings
- baseline
- big data