Law Probability & Risk最新文献

From specific-source feature-based to common-source score-based likelihood-ratio systems: ranking the stars 从基于特定来源的特征到基于共同来源的分数的似然比系统:对星星进行排名

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2023-05-12 DOI: 10.1093/lpr/mgad005

P. Vergeer

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引用次数: 1

Information economics in the criminal standard of proof 刑事证据标准中的信息经济学

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2023-03-14 DOI: 10.1093/lpr/mgad004

Christian Dahlman, A. Nordgaard

引用次数: 1

Odds ratios as a measure of disproportionate treatment: application to jury venires 比值比作为衡量过度治疗的指标：应用于陪审团鹿肉

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2023-03-13 DOI: 10.1093/lpr/mgad003

J. Kadane

引用次数: 0

Inconclusive conclusions in forensic science: rejoinders to Scurich, Morrison, Sinha and Gutierrez 法医学中的不确定性结论：对斯库里奇、莫里森、辛哈和古铁雷斯的反驳

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2023-02-28 DOI: 10.1093/lpr/mgad002

H. Arkes, J. Koehler

引用次数: 1

Signal detection theory fails to account for real-world consequences of inconclusive decisions 信号检测理论无法解释不确定决策的现实后果

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2023-02-07 DOI: 10.1093/lpr/mgad001

Maneka Sinha, Richard Gutíerrez

引用次数: 3

An epistemic theory of the criminal process, Part II: Packer, Posner and epistemic pressure 刑事过程的认识论，第二部分:帕克、波斯纳与认识论压力

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2022-12-23 DOI: 10.1093/lpr/mgac014

William Cullerne Bown

{"title":"An epistemic theory of the criminal process, Part II: Packer, Posner and epistemic pressure","authors":"William Cullerne Bown","doi":"10.1093/lpr/mgac014","DOIUrl":"https://doi.org/10.1093/lpr/mgac014","url":null,"abstract":"\u0000 This is Part II of a novel ‘epistemic’ theory of the criminal process. Part I, a formal treatment of how poor measurements degrade control over systems of classification, was published in the last issue. This distinguished four distinct realms of control a policymaker may inhabit and established that the criminal process in the USA is trapped in the third best of these, routinely unable to establish that any policy is better than any other. Here, I first use the new epistemic concepts to mathematize Packer’s influential ‘two models’ theory, both a validation of the formal work and a way of giving it traction in the real world. I then use the new techniques to critique Posner’s law and economics and reject it on methodological grounds. I then argue that the activity of making policy for the criminal process, including any quantativity, is largely a form of ritual. The function of this has been to keep at bay an enduring pressure for rationality, as in Weber’s rationalization thesis, that could not be satisfied. Now that we have the capacity for better measurements, a rational approach is for the first time attainable, and I contemplate the end of the old order.","PeriodicalId":48724,"journal":{"name":"Law Probability & Risk","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46926438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A plague on both your houses: The debate about how to deal with ‘inconclusive’ conclusions when calculating error rates 一场瘟疫困扰着你的两家：关于如何在计算错误率时处理“不确定”结论的辩论

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2022-11-23 DOI: 10.1093/lpr/mgac015

G. Morrison

引用次数: 3

Erosion of the basic human rights 基本人权受到侵蚀

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2022-11-16 DOI: 10.52340/law.2022.01

Nugzar Saria

引用次数: 0

Inconclusives are not errors: a rejoinder to Dror 结论不是错误：对Dror的反驳

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2022-10-12 DOI: 10.1093/lpr/mgac009

H. Arkes, J. Koehler

引用次数: 5

‘This Crime is Not That Crime’—Classification and evaluation of four common crimes “本罪非彼罪”——四种常见犯罪的分类与评价

IF 0.7 4区社会学

Law Probability & Risk Pub Date : 2022-07-14 DOI: 10.1093/lpr/mgac006

K. Xu, Han Liu, Fang Wang, Han Wang

{"title":"‘This Crime is Not That Crime’—Classification and evaluation of four common crimes","authors":"K. Xu, Han Liu, Fang Wang, Han Wang","doi":"10.1093/lpr/mgac006","DOIUrl":"https://doi.org/10.1093/lpr/mgac006","url":null,"abstract":"\u0000 As the basis of criminal penalty, criminal conviction, integral to the protection of fundamental rights and freedom of people constitutes the basis and the core issue of criminal trials. Based on the data published on China Judgments Online, we proposed two types of classification models to apply the data of four common crimes from China Judgments Online and expounded their applications in identifying ‘abnormal cases’, defined as wrongly sentenced cases in this article. The two types of classification models we proposed are a two-stage model and two deep learning models. To construct the two-stage model, we first used three keyword-extraction models to extract the keywords and vectorize all the keywords, then used five classification models to build the two-stage model. For the deep learning models, we applied two different deep neural network models in the data to build the classifier. We then applied these two types of classification models to discover ‘abnormal cases’ in two steps. In the first step, we applied the two-stage model to extract the ‘important words’ which will significantly improve the probability of the two-stage model to classify cases into crimes of intentional injury. In the second step, we constructed a validation data set of cases whose verdicts are changed in the second instance rulings to test the ‘important words’ extracted in first step and the ability of the two-stage model and the two deep learning models to discover ‘abnormal cases’. The results of this exercise show that: (1) ‘important words’ extracted in the first step are often associated with ‘abnormal cases’; (2) these two types of classification models can effectively discover ‘abnormal cases’, but compared with the two deep learning models, the two-stage model (aka. Term Frequency-Inverse Document Frequency and Artificial Neural Network, the combination of a keyword extraction model and a classic machine-learning model) is more capable of discovering ‘abnormal cases’.","PeriodicalId":48724,"journal":{"name":"Law Probability & Risk","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44673809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1