Online research in philosophy

The distinction between data and phenomena introduced by Bogen and Woodward (Philosophical Review 97(3):303–352, 1988) was meant to help accounting for scientific practice, especially in relation with scientific theory testing. Their article and the subsequent discussion is primarily viewed as internal to philosophy of science. We shall argue that the data/phenomena distinction can be used much more broadly in modelling processes in philosophy.

Depending on different positions in the debate on scientific realism, there are various accounts of the phenomena of physics. For scientific realists like Bogen and Woodward, phenomena are matters of fact in nature, i.e., the effects explained and predicted by physical theories. For empiricists like van Fraassen, the phenomena of physics are the appearances observed or perceived by sensory experience. Constructivists, however, regard the phenomena of physics as artificial structures generated by experimental and mathematical methods. My paper investigates the historical background of these different meanings of phenomenon in the traditions of physics and philosophy. In particular, I discuss Newton’s account of the phenomena and Bohr’s view of quantum phenomena, their relation to the philosophical discussion, and to data and evidence in current particle physics and quantum optics.

This paper investigates some metaphysical and epistemological assumptions behind Bogen and Woodward’s data-to-phenomena inferences. I raise a series of points and suggest an alternative possible Kantian stance about data-to-phenomena inferences. I clarify the nature of the suggested Kantian stance by contrasting it with McAllister’s view about phenomena as patterns in data sets.

In this paper I offer an appraisal of James Bogen and James Woodward’s distinction between data and phenomena which pursues two objectives. First, I aim to clarify the notion of a scientific phenomenon. Such a clarification is required because despite its intuitive plausibility it is not exactly clear how Bogen and Woodward’s distinction has to be understood. I reject one common interpretation of the distinction, endorsed for example by James McAllister and Bruce Glymour, which identifies phenomena with patterns in data sets. Furthermore, I point out that other interpretations of Bogen and Woodward’s distinction do not specify the relationship between phenomena and theories in a satisfying manner. In order to avoid this problem I propose a contextual understanding of scientific phenomena according to which phenomena are states of affairs which play specific roles in scientific practice and to which we adopt a special epistemic attitude. Second, I evaluate the epistemological significance of Bogen and Woodward’s distinction with respect to the debate between scientific realists and constructive empiricists. Contrary to what Bogen and Woodward claim, I argue that the distinction does not provide a convincing argument against constructive empiricism.

Bogen and Woodward claim that the function of scientific theories is to account for 'phenomena', which they describe both as investigator-independent constituents of the world and as corresponding to patterns in data sets. I argue that, if phenomena are considered to correspond to patterns in data, it is inadmissible to regard them as investigator-independent entities. Bogen and Woodward's account of phenomena is thus incoherent. I offer an alternative account, according to which phenomena are investigator-relative entities. All the infinitely many patterns that data sets exhibit have equal intrinsic claim to the status of phenomenon: each investigator may stipulate which patterns correspond to phenomena for him or her. My notion of phenomena accords better both with experimental practice and with the historical development of science.

Bogen and Woodward characterized data as embedded in the context in which they are produced (‘local’) and claims about phenomena as retaining their significance beyond that context (‘nonlocal’). This view does not fit sciences such as biology, which successfully disseminate data via packaging processes that include appropriate labels, vehicles, and human interventions. These processes enhance the evidential scope of data and ensure that claims about phenomena are understood in the same way across research communities. I conclude that the degree of locality of both data and claims about phenomena varies depending on the packaging used to make them travel and on the research setting in which they are used. †To contact the author, please write to: ESRC Centre for Genomics in Society, University of Exeter, Byrne House, St. Germans Road, EX4 4PJ Exeter, United Kingdom; e‐mail: s.leonelli@exeter.ac.uk.

Bogen and Woodward (1988) advance adistinction between data and phenomena. Roughly, theformer are the observations reported by experimentalscientists, the latter are objective, stable featuresof the world to which scientists infer based onpatterns in reliable data. While phenomena areexplained by theories, data are not, and so theempirical basis for an inference to a theory consistsin claims about phenomena. McAllister (1997) hasrecently offered a critique of their version of thisdistinction, offering in its place a version on whichphenomena are theory laden, and hence on which theempirical support for inferences to theories is also,unavoidably, theory laden. In this commentary I arguethat McAllister and Bogen and Woodward are mistaken inthinking that the distinction is necessary, and thatthe empirical support for inferences to theories isnot necessarily theory laden in the way McAllister'saccount entails they are.

Some twenty years ago, Bogen and Woodward challenged one of the fundamental assumptions of the received view, namely the theory-observation dichotomy and argued for the introduction of the further category of scientific phenomena. The latter, Bogen and Woodward stressed, are usually unobservable and inferred from what is indeed observable, namely scientific data. Crucially, Bogen and Woodward claimed that theories predict and explain phenomena, but not data. But then, of course, the thesis of theory-ladenness, which has it that our observations are influenced by the theories we hold, cannot apply. On the basis of two case studies, I want to show that this consequence of Bogen and Woodward’s account is rather unrealistic. More importantly, I also object against Bogen and Woodward’s view that the reliability of data, which constitutes the precondition for data-to-phenomena inferences, can be secured without the theory one seeks to test. The case studies I revisit have figured heavily in the publications of Bogen and Woodward and others: the discovery of weak neutral currents and the discovery of the zebra pattern of magnetic anomalies. I show that, in the latter case, data can be ignored if they appear to be irrelevant from a particular theoretical perspective (TLI) and that, in the former case, the tested theory can be critical for the assessment of the reliability of the data (TLA). I argue that both TLI and TLA are much stronger senses of theory-ladenness than the classical thesis and that neither TLI nor TLA can be accommodated within Bogen and Woodward’s account.

1. 'Data Meet Theories: Up Close and Personal' - In this talk I extend my critique of Bogen and Woodward's claim that we do not (and perhaps cannot) use theories to infer, predict or explain observations. I do so by demonstrating that paradigmatic cases of novel prediction could not have been made unless the relationship between data and theories is more direct than Bogen and woodward would have us believe. (To be presented at the conference Data - Phenomena - Theories: What's the notion of a scientific phenomenon good for?, University of Heidelberg , September 11-13 2008).

Jim Bogen and James Woodward’s ‘Saving the Phenomena’, published only twenty years ago, has become a modern classic. Their centrepiece idea is a distinction between data and phenomena. According to them, data are typically the kind of things that are observable or measurable like “bubble chamber photographs, patterns of discharge in electronic particle detectors and records of reaction times and error rates in various psychological experiments” (p. 306). Phenomena are physical processes that are typically unobservable. Examples of the latter category include “weak neutral currents, the decay of the proton, and chunking and recency effects in human memory” (ibid.). Theories, in Bogen and Woodward’s view, are utilised to systematically explain and predict phenomena, not data (pp. 305-306). The relationship between theories and data is rather indirect. Data count as evidence for phenomena and the latter in turn count as evidence for theories. This view has been further elaborated in subsequent papers (see Bogen and Woodward 1992, 2005 and Woodward 1989) and is becoming increasingly influential (e.g. Prajit K. Basu 2003, Stathis Psillos 2004 and Mauricio Suárez 2005). In this paper I argue that in various significant and well-known cases theories accompanied with suitable auxiliary hypotheses are more proximal to observations than Bogen and Woodward would have us believe. This is especially true of cases involving novel predictions.