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  Subjects -> STATISTICS (Total: 130 journals)
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Law, Probability and Risk
Journal Prestige (SJR): 0.777
Citation Impact (citeScore): 1
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1470-8396 - ISSN (Online) 1470-840X
Published by Oxford University Press Homepage  [419 journals]
  • ‘This Crime is Not That Crime’—Classification and
           evaluation of four common crimes

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      Pages: 135 - 152
      Abstract: AbstractAs 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’.
      PubDate: Thu, 14 Jul 2022 00:00:00 GMT
      DOI: 10.1093/lpr/mgac006
      Issue No: Vol. 20, No. 3 (2022)
       
  • Inconclusives and error rates in forensic science: a signal detection
           theory approach

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      Pages: 153 - 168
      Abstract: AbstractThere are times when a forensic scientist may not be comfortable drawing a firm conclusion about whether a questioned sample that appears to contain useful identifying information did or did not come from a particular known source. In such cases, the forensic scientist may call the sample pair ‘inconclusive’. We suggest that signal detection theory (SDT), which is concerned with the detection of weak signals in noisy environments, provides a useful framework for understanding the role that inconclusives play in the various feature-matching forensic sciences. SDT shows that ‘inconclusive’ is often an appropriate response depending on both the strength of the signal in the samples and the thresholds adopted by the examiner. We also argue that inconclusives should not be coded as either correct or incorrect when tabulating forensic error rates.
      PubDate: Tue, 28 Jun 2022 00:00:00 GMT
      DOI: 10.1093/lpr/mgac005
      Issue No: Vol. 20, No. 3 (2022)
       
  • Statistical analyses in the case of an Italian nurse accused of murdering
           patients

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      Pages: 169 - 193
      Abstract: AbstractSuspicions about medical murder sometimes arise due to a surprising or unexpected series of events, such as an apparently unusual number of deaths among patients under the care of a particular nurse. But also a single disturbing event might trigger suspicion about a particular nurse, and this might then lead to investigation of events which happened when she was thought to be present. In either case, there is a statistical challenge of distinguishing event clusters that arise from criminal acts from those that arise coincidentally from other causes. We show that an apparently striking association between a nurse’s presence and a high rate of deaths in a hospital ward can easily be completely spurious. In short: in a medium-care hospital ward where many patients are suffering terminal illnesses, and deaths are frequent, most deaths occur in the morning. Most nurses are on duty in the morning, too. There are less deaths in the afternoon, and even less at night; correspondingly, less nurses are on duty in the afternoon, even less during the night. Consequently, a full time nurse works the most hours when the most deaths occur. The death rate is higher when she is present than when she is absent.
      PubDate: Mon, 12 Sep 2022 00:00:00 GMT
      DOI: 10.1093/lpr/mgac007
      Issue No: Vol. 20, No. 3 (2022)
       
 
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