Hitotsubashi University

Examining the skilled migration of IT workers from South Korea to Japan, this study illuminates the critical role of the sending country government in steering migration intermediaries and migrants towards less-than-ideal employment outcomes in the destination country. By analysing the three phases of pre- and post-migration mediation – attraction, allocation and integration – and examining the interactions among multiple actors in each phase, the study uncovers how the continuous flow of IT skilled migration from South Korea to Japan since around 2000 has produced skilled yet flexible migrant labour in Japan’s expanding IT industry. This study advances theoretical discussions on the nexus between migrant labour and the flexible labour market structures of the host country by shedding light on the role of sending states in constructing ‘supply-driven’ skilled yet flexible labour mobility.

Artificial intelligence (AI) tools based on large language models (LLMs) are being increasingly used by researchers and may play a role in health-related research priority-setting exercises (RPSEs). However, little is known about how these tools may differ in the types of research priorities they generate. We examined research priorities aimed at improving treatments for four diseases: cancer, COVID-19, HIV, and Alzheimer. We compared the outputs from five AI tools (DeepSeek, ChatGPT, Claude, Perplexity, and Gemini) using SBERT-BioBERT embeddings and cosine similarity scores, and assessed the stability of differences between them by re-running identical prompts and slightly modified versions. We found that the outputs produced by Gemini were highly similar to those produced by the other tools. The two most different outputs were those produced by DeepSeek and Perplexity, whereby the former tended to emphasise technical medical issues, while the latter emphasised public health concerns. This substantive distinction between DeepSeek and Perplexity remained stable across repeated and tweaked prompts. Our exploratory analysis suggests that Gemini performs well for researchers who prefer to generate health-related research priorities using a single AI model. For those planning to draw on multiple models, Perplexity and DeepSeek offer complementary perspectives.

ABSTRACTThis study explored the reciprocal associations between maternal physical discipline and children's externalizing problems across childhood, distinguishing between trait‐like (i.e., between‐person) and state‐like (i.e., within‐person) differences. We used data from 604 dyads in Singapore, a context where physical discipline is commonly accepted. We analyzed maternal physical discipline and children's externalizing problems at three timepoints between ages 4 and 10.5 using a random‐intercept cross‐lagged panel model, adjusting for sociodemographic factors. At the between‐person level, maternal physical discipline covaried positively with more externalizing problems in children, supporting extensive evidence in the literature. However, at the within‐person level, maternal physical discipline at age 4.5 predicted children's lower externalizing problems at age 7. These findings underscore the value of disentangling between‐ and within‐person effects and highlight the complex associations between physical discipline and externalizing problems in a setting where physical discipline is common.

本研究では，医療材料SPDにおける業務委託について，業務範囲の観点から定量的に導入状況・評価を明らかにすることを目的として，2023年に実施した２回のアンケート調査回答を連結して分析した。