K10 - Workshop: Science, Evidence and Policy (1)
Date: May 31 | Time: 10:30am to 12:00pm | Location: Classroom - CL 316 Room ID:15727
Chair/Président/Présidente : Patrick Fafard (University of Ottawa)
Discussant/Commentateur/Commentatrice : Jeremy Rayner (University of Saskatchewan)
Imagining Risk: Images of Children, Child Pornography and the COPINE Scale: Carol Dauda (University of Guelph)
Abstract: This paper considers two policy issues in the use of risk assessment in the current handling of child pornography on the Internet in Canada, the UK, and Australia within the wider context of generational relations of childhood to adulthood and child abuse. First, policy makers have cast a very wide net of zero tolerance in the definition of child pornography to include imagined material which, they argue, represents the risk of harm to children. Zero tolerance broadens the opportunity for potential arrests and adds to the ‘quantity’ of evidence so aids in reducing that risk. Second, they use some version of the COPINE scale, developed by psychologists as a graduating scale of depicted images and acts, to assess the ‘quality’ of the material for sentencing purposes. The gauging of severity includes a risk assessment of whether the possessor may move to an actual contact offence. The paper argues that concentration on images not only provokes and reinforces symbolic ideas of childhood vulnerability but also distracts us from the actual generational relations of power involved in abuse. In addition, the evidence from research on perpetrators of child abuse not only questions the viability of the COPINE scale but also the dubious practice of using it to punish a potential crime. Both policies divert energy and resources from confronting the actual circumstances of child abuse, creating a different kind of risk for children in difficult circumstances.
Debating Evidence: Legitimacy, Science and Expertise in Federal Environmental Assessment: Luc Juillet (University of Ottawa)
Abstract: The role of scientific evidence in environmental management has long been a controversial issue. However, in recent years, debates about how to optimize the contribution of science to environmental decision-making have been particularly important in the field of environmental assessment. Calls for more “science-based decisions” have been prominent in many federal environmental assessment controversies, such as the approval of new pipelines, and have been made both by critics and proponents of contested projects. While the need for paying more attention to evidence seems widespread across political divides, there does not seem to be much consensus on what evidence-based decision actually means or on how to optimize the integration of scientific evidence in decision-making. In this paper, we will draw on a content/discursive analysis of about 100 briefs submitted to the federal Expert Panel on the Review of Environmental Assessment Processes from October 2016 to March 2017 to critically examine how stakeholders define evidence and how they frame its role in project approval decisions. We will show that, despite widely shared support for more “science-based” decisions, industry associations, environmentalists, government departments and indigenous organisations differ in significant ways in what they consider to be relevant/legitimate evidence, the range of scientific evidence to be considered and how to justify the varying importance given to scientific evidence in making project decisions.
What Does It Mean to Support Causal Policy Claims with Science? An Epistemological Analysis of Some Foundational Issues: Mathieu Ouimet (Université Laval), Pierre-Olivier Bédard (Université Laval)
Abstract: Evidence-based decision-making involves a set of philosophical and epistemological views regarding the attributes of scientific knowledge that are not always explicit. This paper aims to clarify the epistemological implications of promoting the use of science in support of causal policy claims about the ‘causes’ of an event/phenomenon and/or about the effect of a policy intervention.The first section describes the structure of scientific explanation by showing that what is being tested is a model composed of concepts (Xs and Ys) linked by propositions that are logically deduced from a set of postulates. What is directly tested are not the linking propositions, but rather ‘verifiable implications’ (VIs) based on untested auxiliary hypotheses (AHs). It is thus problematic to consider research findings as probably true or probably false, as testing a causal model is showing that context-specific empirical observations are in line (or not) with VIs. The second section revisits the polysemic concept of ‘truth’: What do promoters of scientific advice consider to be ‘true’ causal policy claims? Claims corresponding to systematic empirical observations? Claims supported by a large number of studies and/or scientists? Claims that are pragmatically useful? As demonstrated through examples, the adopted definition of truth has direct implications on what people might consider as scientific or unscientific causal policy claims. Building on the implications of various theories of truth, the third section of the paper revisits the demarcation problem in philosophy of science and analyzes its practical implications for the use of science in policy.