Ecole des Sciences Criminelles, Université de Lausanne, 1015, Lausanne, Switzerland; Department of forensic science and crime intelligence, Police neuchâteloise, Switzerland; Forensic Research Group (LRC), Université du Québec à Trois-Rivières, Canada.
Centre for Forensic Science, University of Technology Sydney, Broadway, NSW, Australia.
Forensic Sci Int. 2020 Apr;309:110213. doi: 10.1016/j.forsciint.2020.110213. Epub 2020 Feb 20.
Forensic science has been evolving towards a separation of more and more specialised tasks, with forensic practitioners increasingly identifying themselves with only one sub-discipline or task of forensic science. Such divisions are viewed as a threat to the advancement of science because they tend to polarise researchers and tear apart scientific communities. The objective of this article is to highlight that a piece of information is not either intelligence or evidence, and that a forensic scientist is not either an investigator or an evaluator, but that these notions must all be applied in conjunction to successfully understand a criminal problem or solve a case. To capture the scope, strength and contribution of forensic science, this paper proposes a progressive but non-linear continuous model that could serve as a guide for forensic reasoning and processes. In this approach, hypothetico-deductive reasoning, iterative thinking and the notion of entropy are used to frame the continuum, situate forensic scientists' operating contexts and decision points. Situations and examples drawn from experience and practice are used to illustrate the approach. The authors argue that forensic science, as a discipline, should not be defined according to the context it serves (i.e. an investigation, a court decision or an intelligence process), but as a general, scientific and holistic trace-focused practice that contributes to a broad range of goals in various contexts. Since forensic science does not work in isolation, the approach also provides a useful basis as to how forensic scientists should contribute to collective and collaborative problem-solving to improve justice and security.
法庭科学正向越来越多专业化任务分离的方向发展，法庭科学从业者越来越倾向于将自己仅定位在法庭科学的一个子学科或任务上。这种分工被视为对科学进步的威胁，因为它容易使研究人员两极分化，并使科学界分裂。本文的目的是强调一个信息既不是情报也不是证据，法庭科学家既不是调查员也不是评估员，但必须结合所有这些概念才能成功理解犯罪问题或解决案件。为了全面、准确和有效地理解法庭科学，本文提出了一个渐进但非线性的连续模型，作为法庭推理和程序的指南。在这种方法中，假设演绎推理、迭代思维和熵的概念被用来构建连续体，确定法庭科学家的操作环境和决策点。本文还从经验和实践中选取了一些情况和实例来说明该方法。作者认为，法庭科学作为一门学科，不应该根据其服务的背景（即调查、法庭裁决或情报过程）来定义，而应该定义为一种通用的、科学的和整体的、以痕迹为中心的实践，为各种背景下的广泛目标做出贡献。由于法庭科学不是孤立工作的，因此该方法还为法庭科学家如何为集体和协作解决问题以提高司法和安全做出贡献提供了有益的基础。
