Global Economic Downturn and Social Protection in East Asia: Pathways of Global and Local Interactions

Author(s):  
Huck-ju Kwon
2021 ◽  
pp. 002073142098669
Author(s):  
Nuria Matilla-Santander ◽  
Emily Ahonen ◽  
Maria Albin ◽  
Sherry Baron ◽  
Mireia Bolíbar ◽  
...  

The world of work is facing an ongoing pandemic and an economic downturn with severe effects worldwide. Workers trapped in precarious employment (PE), both formal and informal, are among those most affected by the COVID-19 pandemic. Here we call attention to at least 5 critical ways that the consequences of the crisis among workers in PE will be felt globally: ( a) PE will increase, ( b) workers in PE will become more precarious, ( c) workers in PE will face unemployment without being officially laid off, ( d) workers in PE will be exposed to serious stressors and dramatic life changes that may lead to a rise in diseases of despair, and ( e) PE might be a factor in deterring the control of or in generating new COVID-19 outbreaks. We conclude that what we really need is a new social contract, where the work of all workers is recognized and protected with adequate job contracts, employment security, and social protection in a new economy, both during and after the COVID-19 crisis.


Author(s):  
Patricia Kennett ◽  
Kam Wah Chan ◽  
Lucille Lok-Sun Ngan
Keyword(s):  

2016 ◽  
Vol 12 ◽  
pp. P306-P306
Author(s):  
Lorenzo Pasquini ◽  
Gloria Benson ◽  
Martin Scherr ◽  
Igor Yakushev ◽  
Timo Grimmer ◽  
...  

2022 ◽  
Vol 40 (3) ◽  
pp. 1-33
Author(s):  
Xingshan Zeng ◽  
Jing Li ◽  
Lingzhi Wang ◽  
Kam-Fai Wong

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.


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