Environmental Science: Empirical Claims in Environmental Ethics 35 are encoded in the very terms used in science, for example, in ecosystem “health” or ecosys- tem “services” (see also Dupré, 2007), and on other subtle ways in which contextual values influence scientific reasoning, even when this is not intended. For instance, Longino (1990) argues that contextual values sometimes shape scientists’ background assumptions, which in turn influence the range of hypotheses that they consider plausible and the extent to which they understand data to provide evidence for a hypothesis (see also Sarewitz, 2004). In this way, the conclusions reached by a scientist may be subtly biased by his or her values. For example, suppose that a scientist values helping people and, in part as a consequence of this, has as an implicit background assumption that most social ills can be mitigated substantially with interventions to the social environment; when analyzing a particular social problem, this scientist may not even consider the hypothesis that a genetic or other non- social cause may be a significant factor.
Longino thus recommends that objectivity be understood as a community- level feature of science: the practices of a scientific community are objective to the extent that they not only allow for criticism of background assumptions, data, and methods from a range of perspec- tives but also are responsive to such criticism (1990: 76– 80; Longino, 2002: 128– 135).8 This is not to suggest that individual scientists should not make an honest effort to find out what the world is like (see also Douglas, 2009, ch. 6). The point, rather, is that critical dialogue with others— whether in the published literature or in more informal venues— brings additional opportunities for uncovering and transcending biases at the individual level.
This is a salutary reminder in the face of entrenched, opposing views on environmental issues. Of course, sometimes uncertainty and controversy are manufactured deliberately, with the aim of forestalling undesirable policy action.9 Likewise, sometimes evidence is deliberately presented in a selective way in order to give a misleading impression of what scientific investigation has uncovered. But other times entrenched disagreement may reflect an unwillingness to question preferred background assumptions or to seriously engage with individuals who, despite some shared standards, nevertheless interpret the available data dif- ferently. When this unwillingness is a persistent feature of a community, it too can be under- stood as a failure of objectivity.
36 Wendy s. Parker
General scepticism about results obtained from models is unwarranted; though a model may differ from its target in salient ways, it may still be adequate for the purposes for which it is used. Indeed, there can be good reason to believe that a model is adequate for a particu- lar purpose, even when some of its results fail to match observations of the target system.
When there is uncertainty about how to build an adequate model, investigative strategies involving multiple models can be useful, but interpreting results can be tricky. For instance, just as consensus among scientists takes on special epistemic significance only under certain circumstances, so does agreement among modeling results.
Debate continues over the appropriate roles of social, political, and ethical values in sci- ence. Arguments from inductive risk see an actual and/ or desirable role for these values when faced with uncertain methodological choices. Opponents contend that appeal to val- ues here can and/ or should be avoided. More broadly, it has been argued that contextual values shape background assumptions in subtle ways that influence the way evidence is evaluated. This has led to the proposal that objectivity be understood as a community- level feature of science, which is achieved to the extent that a community allows and is responsive to criticism of background assumptions, data, and methods from a range of perspectives.
Notes
1. According to Bayes’ Theorem: p H e( | )=p H x p e H p e( ) ( | ) ( )/ , where p(H|e) is the prob- ability that the agent should assign to H if e is obtained, i.e. the updated probability for H;
p(H) is the probability that the agent assigns to H before obtaining e; p(e|H) is the prob- ability of obtaining e if H is true; and p(e) is the probability of obtaining e whether H is true or false, i.e. the expectedness of e. This simple formulation of Bayes’ Theorem leaves implicit the role of background information, b; including it results in a slightly different formulation: p H e b( | & )=p H b x p e H b p e b( | ) ( | & /) ( | ).
2. Such convergence is not guaranteed. For instance, if scientists often disagree on whether p e H( | )>p e( ) for new pieces of evidence, their probabilities might not converge.
3. Situations in which probabilities cannot be assigned to hypotheses are also known as situ- ations of Knightian uncertainty (see Knight, 1921).
4. Brysse et al. (2012) suggest that in fact the scientific community has a tendency to “err on the side of least drama”— to “demand greater levels of evidence in support of surprising, dramatic, or alarming conclusions than in support of conclusions that are less surprising, less alarming, or more consistent with the scientific status quo” (pp. 327– 328).
5. It is interesting that the EPA guidelines for carcinogen risk assessment (2005), mentioned earlier, provide a number of ‘default options’ that can be employed in situations of meth- odological uncertainty; these options are said to be “consistent with EPA’s mission to pro- tect human health while adhering to the tenets of sound science” (pp. 1– 7).
6. Note that Betz does not argue that the value- free ideal should be adopted; he simply argues that contextual value judgments can be avoided in the face of methodological uncertainty.
7. Biddle and Winsberg (2010) argue that contextual values also influence which climate model variables are prioritized for accurate simulation and that the effects of this prioriti- zation cannot be removed from probabilistic estimates of uncertainty about future climate change (see also Winsberg, 2012; Parker, 2014).
8. Note that in such a community, consensus on policy- relevant scientific questions may indeed be “hard won” (see under Evidence, Uncertainty, and Consensus).
Environmental Science: Empirical Claims in Environmental Ethics 37 9. For examples in public health (e.g., tobacco, asbestos) and climate change, see Michaels,
2006; Edwards, 2010, ch. 15; Oreskes and Conway, 2011.
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