Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology.
The uncertainty of judicial decision-making has a deep and extensive theoretical foundation. Theoretical analysis starts with a reflection on legal rationalism that challenges the legal certainty before delving deeply into the case's facts and the entire legal system. In light of this, this paper ex...
| Publicado en: | Journal of Environmental & Public Health pp. 1 - 10 |
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| Autor principal: | |
| Formato: | Journal Article |
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Wiley-Blackwell
9/6/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158930278&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158930278 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16879805 9034 jtl: Journal of Environmental & Public Health issn: 16879805 maglogo: N pubinfo: dt: 9/6/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158930278 158930278 NLM36111066 10.1155/2022/1088046 NLM36111066 158930278 ppf: 1 ppct: 9 formats: tig: atl: Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology. aug: au: Li, Yongchao affil: Zhengzhou University School of Law, Zhengzhou 450001, China sug: ab: The uncertainty of judicial decision-making has a deep and extensive theoretical foundation. Theoretical analysis starts with a reflection on legal rationalism that challenges the legal certainty before delving deeply into the case's facts and the entire legal system. In light of this, this paper explores a novel approach to enhance the reasoning mechanism of trial documents from the viewpoint of modern cognitive psychology, concentrating on the parties' and the public's cognitive processes to justice. It is suggested to use an inert hierarchical multilabel classification algorithm. In order to predict the category of invisible examples, the extended multilabel training set is first searched for adjacent samples of invisible examples, and the classification weight and confidence of each category are then determined in accordance with these adjacent samples. The group of invisible examples is then anticipated. Experimental comparison demonstrates that this algorithm outperforms other prediction techniques; the macro accuracy, macro recall, and macro F1 of this method are, respectively, 0.896, 0.871, and 0.814. It has some advantages in many multilabel evaluation indexes when compared to other multilabel algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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