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    • br The value of a model of the complex forensic

    2022-05-20


    The value of a model of the complex forensic science matrix There have been various models proposed to address the complexity inherent to forensic reconstruction [15], [16], [17]. It is possible to argue that embracing the value of holistic models is important if we are to reclaim the ‘endeavour of forensic science’ [14] in a manner that brings both explicit and tacit forms of knowledge together [18] in forensic reconstruction approaches [15]. Such models that seek to identify critical components and how they interact with each other are constructive and beneficial. They enable us to identify what is known and what is not known; they direct us to where the gaps are that need to be filled and thereby to areas of research need; they help us to identify and explain what the ‘known unknowns’ are and where the ‘unknown unknowns’ are likely to be; and they help us to identify where there is inherent uncertainty which helps us to develop the frameworks that are needed to explain that uncertainty (and to know when it Agarose GPG/LMP low melt synthesis is, and is not, a problem in forensic reconstruction). Models that provide a holistic overview of a complex system are therefore valuable in presenting a view of the broad picture and unifying themes, and also the diverse factors and variables that are integral to that system. They offer the means to bring the ‘hedgehog’ and ‘fox’ approaches together. These models can provide valuable context to a specific issue and thereby offer insights into the type of solution that is most likely to be effective. They can reveal the nature of specific challenges, and thus the type of knowledge on the explicit tacit knowledge continuum that is needed to address them [19]. Broad models can also help to articulate what form of knowledge is being used to underpin the practices at the crime scene, in the lab and how those findings are expressed to investigators and to a court. These insights can increase the transparency of how findings have been reached, and the basis for inferences that have led to our understanding of what an exhibit or findings from the analysis of specimens means in a specific context. For example, quality standards and regulation are critical to ensuring the delivery of robust and accurate analysis [20]. Situating the (often) explicit forms of knowledge that contribute to standard operating procedures and quality standards within a holistic model of forensic reconstruction provides the means to pinpoint the forms of knowledge these processes and standards are based on, and therefore the issues that they can directly address, and also those that they cannot. In a similar way judgement and decision making is a critical component that permeates through the whole forensic science matrix [21], [22]. Holistic models can demonstrate the integral nature of decision making to every part of a forensic reconstruction. They can offer transparency in terms of where decision making is critical, the type of knowledge underpinning different types of decision, where there is inherent uncertainty, and the extrinsic factors (such as context) and intrinsic factors (such as prior experience) that can impact a decision. Ultimately holistic models of forensic reconstruction provide an overview of the complex matrix of forensic reconstruction, and can identify where there may be uncertainty that will impact the various stages within the forensic process, the inferences that are made, and the conclusions that can be drawn. To achieve robust reconstructions and to address the challenges we are facing in forensic science, the strengths of the ‘hedgehog’ and ‘fox’ approaches need to be incorporated into our problem solving approaches. ‘Hedgehogs’ tend to have a very clear consolidated view of a topic or a challenge, which reduces uncertainty in favour of offering a clear ‘solution’ to the challenge. In contrast ‘foxes’ are more complex thinkers, at ease with the idea that outcomes often emerge from the interactions of multiple variables (different actors, institutions, forces) that are often difficult to predict [23]. Therefore, bringing both approaches together increases our ability to offer solutions to specific challenges that are more likely to have impact.