 However, the calibration approach has also elicited criticism. Goldman (1986) asks us to imagine an agent A, 70% of whose opinions turn out to be true. A can achieve a perfectly calibrated credence function by adopting a .7 credence in every proposition about which she has an opinion. However, Goldman argues that it’s wrong to automatically conclude that A’s credal state is perfectly reliable. If A has no good reason for adopting a .7 credence in many of the propositions in question, then her credal state shouldn’t count as justified. Dunn (2015) defends the calibration approach, arguing that the relevant question is whether the process that produced A’s credal state is reliable. In order to answer this question, it’s not enough to look at the truth-ratio of A’s opinions at the actual world; rather, we should look across a range of nearby worlds. If it’s just a matter of chance that 70% of the propositions A has an opinion about are true, then by looking at the truth-values of A’s opinions at nearby worlds the calibration approach will be able to avoid the counterintuitive consequence that A’s credal state is perfectly reliable.