• Korean Journal of Social Welfare
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• v.59 no.4
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• pp.273-296
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• 2007

In this Study, to reconstruct the disability identity scale(Lee and Shin, 2006)), Rasch rating scale model was applied to the four sub-dimensions of the Disability Identity Scale in a sample of spinal cord injuries(N=397). The Disability Identity Scale was verified by explorative factor analysis and confirmatory factor analysis. However, factor analytic procedures can't evaluate item-fit indices, item difficulty, and appropriate scale category. A number of limitations posed by confirmatory factor analytic procedures can be averted with the use of Rasch rating scale model which is in the item response theory(IRT). So in this study, Rasch model was applied to the Disability Identity Scale. Results revealed that (A) 20 items were selected from Rasch model, (B) the difficulty level of the Disability Identity Scale was the average level, (C) 4-point rating scale was appropriate for the Disability Identity Scale. Finally, we could suggest that the sub-dimensions concepts of the disability identity became clearer and items were to the good fitting.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.337-347
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• 2016

This paper investigates how social trust and disability identity influence subjective health, focusing on the moderating effect of social-economic status of people with disabilities. We used surveys used for the development of the social exclusion scale of people with disabilities. As a result, social trust and disability identity influence subjective health. Namely, those who have more social trust and high disability identity experience subjective good health than other people with disabilities do. Second, the relationship between disability identity and subjective health was moderated by social-economic status of people with disabilities. Namely, the relationship between disability identity and subjective health is bigger as the level of social-economic status is higher. However, the relationship between social trust and self-rated health was not moderated by social-economic status of people with disabilities. Based on these findings, we suggest policy and practice ways to promote the subjective health status of people with disabilities.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.