Science Ethics and Policy
My core message in this area is: scientific integrity is professionalism.
Professionalism in Science: Competence, Autonomy, and Service
What does it mean when we talk about "professionalizing" scientific and academic research? In this paper I draw on the sociology of the professions to examine what precisely the model of professionalism implies for scientific research. I argue that professionalism, more than any other single organizational logic, is appropriate for scientific research.
Published in Science and Engineering Ethics in 2019.
Research Integrity Codes of Conduct in Europe: Understanding the Divergences
In this study we document in detail how national-level codes diverge on almost all aspects concerning research integrity. This raises fundamental questions about the envisaged function of the ethical content in codes of conduct. We argue that policy-makers need criteria, based on sociological research, on how to deliberate and decide on what to include in a code of conduct.
Forthcoming in Bioethics. Manuscript here .
Philosophy of Science
I revisit some core concerns of philosophy of science -- causation & explanation, methodology, social structure -- and show how core aspects of professionalism (discretion & service) should inform these concerns.
Towards an Ethics of Expert Communication
Too often it is thought, even by the experts themselves, that all they need do is to speak in the name of science. However, the true challenge for good expert communication lies in extracting the appropriate message from the scientific state-of-the-art. Since this involves an important ethical dimension, in this exploratory piece I outline the need for an ethics of expert communication, and what this might entail.
Why Disciplinary Law is the appropriate legal framework for dealing with research misconduct
Using the concept of medical negligence for a concept of scientific negligence
Incentivizing Replication is Insufficient to Safeguard Default Trust
Philosophers of science and meta-scientists alike now typically model scientists’ behavior as driven by credit maximization. In this paper I argue that this modeling assumption cannot account for how scientists have a default level of trust in each other’s assertions. The normative implication of this is that science policy should not only focus on incentive reform.
Manuscript here (accepted in Philosophy of Science)
Shades of Grey: Granularity, Pragmatics, and Non-causal Explanation
It is puzzling why re-describing the phenomenon should make any difference for the causal nature of the favored explanation. I argue that this is a problem for the ontic framework of causal and noncausal explanation, and instead propose a pragmatic-modal account of causal and non-causal explanation.
Published in Perspectives on Science in 2019.