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- Publisher Website: 10.1017/S0305741021001077
- Scopus: eid_2-s2.0-85120915079
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Article: Decoding Political Trust in China: A Machine Learning Analysis
Title | Decoding Political Trust in China: A Machine Learning Analysis |
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Authors | |
Keywords | China machine learning political trust trust in the central government trust in the Centre trust in the local government |
Issue Date | 2022 |
Citation | China Quarterly, 2022, v. 249, p. 1-20 How to Cite? |
Abstract | Survey results inflate political trust in China if the observed trust in the central government is mistaken for the latent trust in the Centre. The target of trust in the country is the Centre, which is ultimately the top leader. The critical issue domain for assessing the Centre's trustworthiness is policy implementation rather than policymaking. The Centre's trustworthiness has two dimensions: commitment to good governance and the capacity to discipline local officials. Observed trust in the central government indicates trust in the Centre's commitment, while observed trust in the local government reflects confidence in the Centre's capacity. A machine learning analysis of a national survey reveals how much conventional reading overestimates political trust. At first glance, 85 per cent of the respondents trust the central government. Upon further inspection, 18 per cent have total trust in the Centre, 34 per cent have partial trust and 33 per cent are sceptical. |
Persistent Identifier | http://hdl.handle.net/10722/344438 |
ISSN | 2023 Impact Factor: 2.5 2023 SCImago Journal Rankings: 0.716 |
DC Field | Value | Language |
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dc.contributor.author | Li, Lianjiang | - |
dc.date.accessioned | 2024-07-31T03:03:30Z | - |
dc.date.available | 2024-07-31T03:03:30Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | China Quarterly, 2022, v. 249, p. 1-20 | - |
dc.identifier.issn | 0305-7410 | - |
dc.identifier.uri | http://hdl.handle.net/10722/344438 | - |
dc.description.abstract | Survey results inflate political trust in China if the observed trust in the central government is mistaken for the latent trust in the Centre. The target of trust in the country is the Centre, which is ultimately the top leader. The critical issue domain for assessing the Centre's trustworthiness is policy implementation rather than policymaking. The Centre's trustworthiness has two dimensions: commitment to good governance and the capacity to discipline local officials. Observed trust in the central government indicates trust in the Centre's commitment, while observed trust in the local government reflects confidence in the Centre's capacity. A machine learning analysis of a national survey reveals how much conventional reading overestimates political trust. At first glance, 85 per cent of the respondents trust the central government. Upon further inspection, 18 per cent have total trust in the Centre, 34 per cent have partial trust and 33 per cent are sceptical. | - |
dc.language | eng | - |
dc.relation.ispartof | China Quarterly | - |
dc.subject | China | - |
dc.subject | machine learning | - |
dc.subject | political trust | - |
dc.subject | trust in the central government | - |
dc.subject | trust in the Centre | - |
dc.subject | trust in the local government | - |
dc.title | Decoding Political Trust in China: A Machine Learning Analysis | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1017/S0305741021001077 | - |
dc.identifier.scopus | eid_2-s2.0-85120915079 | - |
dc.identifier.volume | 249 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 20 | - |
dc.identifier.eissn | 1468-2648 | - |