Samantha Wakil

This paper argues for two conclusions: (1) evaluating the success of engineered concepts necessarily involves empirical work; and (2) the Carnapian Explication criterion precision ought to be a methodological standard in conceptual engineering. These two conclusions provide a new analysis of the race and gender debate between Sally Haslanger and Jennifer Saul. Specifically, the argument identifies the resources Haslanger needs to respond to Saul’s main objections. Lastly, I contrast the methodology advocated here with the so-called “method of cases” and draw out some general implications for how we should think about concepts.

The niche is allegedly the conceptual bedrock underpinning the most prominent, and some would say most important, theorizing in ecology. We argue this point of view is more aspirational than veridical. Rather than critically dissect existing definitions of the concept, the supposedly significant work it is thought to have done in ecology is our evaluative target. There is no denying the impressive mathematical sophistication and theoretical ingenuity of the ecological modeling that invokes ‘niche’ terminology. But despite the pervasive labeling, we demonstrate that niche talk is nothing more than a gloss on theory developed without it, that doesn’t need it, and that doesn’t benefit from it.

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts.

The conservative idea that it is a philosopher’s job to clarify common sense beliefs about ordinary concepts is being weeded out from the population and replaced by a revisionist agenda: philosophers should not merely describe but also analyze and suggest ways to improve our concepts. This project is called "conceptual engineering." The conceptual engineering literature is growing rapidly as more philosophers undertake normative conceptual work. However, many philosophers are practicing conceptual engineering untethered to an explicit methodology. Analyses addressing how we should engineer a concept and what should be considered successful are scarce. The thesis of this dissertation is that Carnapian Explication solves this methodological deficiency.

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts.

This paper argues for two conclusions: (1) evaluating the success of engineered concepts necessarily involves empirical work; and (2) the Carnapian Explication criterion precision ought to be a methodological standard in conceptual engineering. These two conclusions provide a new analysis of the race and gender debate between Sally Haslanger and Jennifer Saul. Specifically, the argument identifies the resources Haslanger needs to respond to Saul’s main objections. Lastly, I contrast the methodology advocated here with the so-called “method of cases” and draw out some general implications for how we should think about concepts.

Pure mathematics can play an indispensable role explaining empirical phenomena if recent accounts of insect evolution are correct. In particular, the prime life cycles of cicadas and the geometric structure of honeycombs are taken to undergird an inference to the best explanation about mathematical entities. Neither example supports this inference or the mathematical realism it is intended to establish. Both incorrectly assume that facts about mathematical optimality drove selection for the respective traits and explain why they exist. We show how this problem can be avoided, identify limitations of explanatory indispensability arguments, and attempt to clarify the nature of mathematical explanation.

Lloyd analyzes every proposed evolutionary explanation of female orgasm and argues that all but one suffers from serious evidential errors. Lloyd attributes these errors to two main biases: androcentrism and adaptationism. This paper begins by arguing that the explanation Lloyd favors—the by-product account—is guilty of the androcentrism which supposedly implicates the other explanations of female orgasm with numerous evidential discrepancies. This suggests that there is another error afflicting orgasm research in addition to the biases Lloyd identities. I attempt to diagnose and characterize this additional error. In short, I think the error is using an imprecise definition of the trait in question. Further, Lloyd takes her analysis to support Longino’s contextual empiricist model of scientific objectivity. I consider what implications this analysis has for contextual empiricism. Finally, I argue that theorizing about female orgasm should be done from a conceptual engineering approach.