Regulating Toxic Substances

Cranor, Carl F.

doi:10.1093/acprof:oso/9780195074369.001.0001

Cited by 137 publications

(28 citation statements)

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“…Both are prone to indeterminacies and uncertainties that have to be managed by extrapolating the available data for real-life exposure situations (Mayo 1991;Cranor 1993). Risk assessment has been characterized by several important methodological controversies, particularly with respect to the burden of proof, inference guides, as well as the standards of evidence (Luján 2005;Luján and Todt 2015;Todt et al 2010).…”

Section: Mechanistic Information In Risk Assessmentmentioning

confidence: 99%

“…While the use of mechanistic information in regulatory science is clearly established in practice, and risk assessment has been extensively studied in philosophy of science (Mayo 1991;Cranor 1993;Machamer and Douglas 1999;Douglas 2000Douglas , 2009Shrader-Frechette 2004;Haack 2008;Wandall et al 2007), there is a dearth of philosophical analysis with respect to the meaning and function of mechanistic information in regulatory science and decision making.…”

Section: Introductionmentioning

confidence: 99%

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Mechanistic Information as Evidence in Decision-Oriented Science

2015

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Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the use of this kind of information in risk assessment is heavily influenced by pragmatic factors, which have not been sufficiently taken into account in philosophical analysis. Mechanistic models are like hypothesis that have to be validated empirically. Due to their dependence on the standards of evidence, they are subject to the same pragmatic factors. Therefore, recurring to mechanistic information does not lead to closure of the methodological controversies in risk assessment.

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Section: Mechanistic Information In Risk Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanistic Information as Evidence in Decision-Oriented Science

2015

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“…The argument from inductive risk asserts that scientists' decisions about what evidence is sufficient for accepting hypotheses can have ethically significant consequences and therefore that ethical values can be relevant to such decisions (Braithewaite, 1953;Churchman, 1948;Cranor, 1993;Douglas, 2000Douglas, , 2009Elliott, 2011Elliott, , 2013Elliott & McKaughan, 2014;Hempel, 1965;Jeffrey, 1956;Levi, 1960Levi, , 1962Levi, , 1967Nagel, 1961;Rudner, 1953;Shrader-Frechette, 1991;Steel, 2010;Steel & Whyte, 2012). One classic line of objection to the argument from inductive risk, due to Jeffrey (1956), insists that accepting and rejecting hypotheses is not the business of scientists.…”

Section: Introductionmentioning

confidence: 99%

Acceptance, values, and probability

Steel¹

2015

Studies in History and Philosophy of Science Part A

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“…1 The function and legitimacy of values in scientific activity is a critically important issue in current philosophy of science (Rudner 1953;Levi 1960;Churchman 1948;Kuhn 1977;Laudan 1984;Worrall 1988;McMullin 1983;Lacey 1999;Machamer and Wolters 2004;Kincaid et al 2007a;Longino 1990;Solomon 2001;Hempel 1981). This debate-whose importance in philosophical analysis has grown over the last decades-also is relevant for some of the more application-oriented areas of science, particularly those that pertain to decision making on technological development, innovation, environmental protection, global change, public health, and related policy concerns (Cranor 1993;Shrader-Frechette 1994;Machamer and Douglas 1999;Douglas 2000Douglas , 2009Lacey 2005). The significance of this issue, in fact, is becoming more acute due to the ever increasing importance of scientific knowledge for policy and decision making (Steele 2012).…”

Section: Introductionmentioning

confidence: 99%

“…One of the domains most concerned is the use of science for regulatory decision making. 2 Value-related debates underlie most of the current controversies with respect to the role of scientific knowledge in the evaluation and management of scientific and technological risks (Murphy et al 2006;Lacey 2005;Douglas 2000;Shrader-Frechette 2001, 2004aCranor 1993Cranor , 2011Steel 2011;Betz 2013;Mayo and Spanos 2006;Elliot and McKaughan 2009). What makes this issue even more pertinent from a philosophical standpoint is that in many senses science, at least when applied to decision making, appears not to be compatible with the traditional ideal of value-free science (Steel 2010(Steel , 2015Elliot and McKaughan 2009;Shrader-Frechette 1994;Cranor 1997;Douglas 2009).…”

Section: Introductionmentioning

confidence: 99%

Non-cognitive Values and Methodological Learning in the Decision-Oriented Sciences

Todt

Luján

2015

Found Sci

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The function and legitimacy of values in decision making is a critically important issue in the contemporary analysis of science. It is particularly relevant for some of the more application-oriented areas of science, specifically decision-oriented science in the field of regulation of technological risks. Our main objective in this paper is to assess the diversity of roles that non-cognitive values related to decision making can adopt in the kinds of scientific activity that underlie risk regulation. We start out, first, by analyzing the issue of values with the help of a framework taken from the wider philosophical debate on science and values. Second, we study the principal conceptualizations used by scholars who have applied them to numerous case studies. Third, we appraise the links between those conceptualizations and learning processes in decision-oriented science. In this, we recur to the concept of methodological learning, i.e., learning about the best methodologies for generating knowledge that is useful for science-based regulatory decisions. The main result of our analysis is that non-cognitive values can contribute to methodological improvements in science in three principal ways: (a) as basis for critical analysis (to differentiate ''sound'' from ''bad'' science), (b) for contextualizing methodologies (by identifying links between methods and objectives), and (c) for establishing the burden of proof (in order to generate data that otherwise would not be generated).

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Regulating Toxic Substances

Cited by 137 publications

References 0 publications

Mechanistic Information as Evidence in Decision-Oriented Science

Mechanistic Information as Evidence in Decision-Oriented Science

Acceptance, values, and probability

Non-cognitive Values and Methodological Learning in the Decision-Oriented Sciences

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