In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited (...) to, biology, and we label it the microarray paradigm. In this new framework, mathematics provides powerful techniques and general ideas which generate new computational tools. But it is missing any explicit isomorphism between a mathematical structure and the phenomenon under consideration. This methodology used in data analysis suggests the possibility of forecasting and analyzing without a structured and general understanding. This is the perspective we propose to call agnostic science, and we argue that, rather than diminishing or flattening the role of mathematics in science, the lack of isomorphisms with phenomena liberates mathematics, paradoxically making more likely the practical use of some of its most sophisticated ideas. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense (...) of data produced in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". (...) A kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate "explicativity", which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the "reduction" of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II. (shrink)

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by (...) presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data (...) science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume (...) first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies. (shrink)

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data (...) analysis techniques, researchers can produce results that are locally robust. Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (shrink)

Introduction. PHilosophy of Language and Linguistics: The Formal Turn Piotr Stalmaszczyk Gottlob Frege, Philosophy of Language, and Predication Piotr Stalmaszczyk Philosophy, Linguistics and Semantic Interpretation Christian Bassac An Unresolved Issue: Nonsense in Natural Language and Non-Classical Logical and Semantic Systems Elzbieta Chrzanowska-Kluczewska Varieties of Context-Dependence Tadeusz Ciecierski The Logos of Semantic Structure Marie Du í, Bjørn Jespersen and Pavel Materna The Good Samaritan and the Hygienic Cook: A Cautionary Tale About Linguistic Data Chris Fox The Meaning (...) of Multiple Quantified Sentences: Where Formal Semantics Meets Psycholinguistics Justyna Grudzinska The Hybrid Theory of Reference for Proper Names Filip Kawczynski On the Nature of Statistical Language Laws Agnieszka Kulacka Vagueness and Contextualism Joanna Odrowaz-Sypniewska The Myth of Semantic Structure Jaroslav Peregrin Scalar Implicatures, Communication and Language Evolution Salvatore Pistoia Reda Semantics and Contextuality: The Case of Pia s Leaves Stefano Predelli Is Logico-Semantical Analysis of Natural Language Expressions a Translation? Jirí Raclavský Beyond the Fregean Myth: The Value of Logical Values Fabien Schang Modal Calculus of Illocutionary Logic Andrew Schumann Subjectivity in Philosophy and Linguistics Barbara Sonnenhauser Asymmetrical Semantics Mieszko Talasiewicz Truth: An Anti-realist Adequacy Condition Luca Tranchini Belief Reports: Defaults, Intentions and Scorekeeping Giacomo Turbanti On Truth in Time Bartosz Wieckowski Order William J. SUllivan Indexes. (shrink)

This paper takes a critical look at theory-free, statistical methodologies for processing and interpreting data taken from respondents answering a set of dichotomous (yes-no) questions. The basic issue concerns to what extent theoretical conclusions based on such analyses are invariant under a class of "informationally equivalent" question transformations. First the notion of Boolean equivalence of two question sets is discussed. Then Lazarsfeld's latent structure analysis is considered in detail. It is discovered that the best fitting latent model depends (...) on which one of the many informationally equivalent question sets is used. This fact raises a number of methodological problems and pitfalls with latent structure analysis. Related problems with other methodologies are briefly discussed. (shrink)

Philosophers of mind, both in the conceptual analysis tradition and in the empirical informed school, have been implicitly neglecting the potential conceptual role of the Peripheral Nervous System (PNS) in understanding sensory and perceptual states. Instead, the philosophical as well as the neuroscientific literature has been assuming that it is the Central Nervous System (CNS) alone, and more exactly the brain, that should prima facie be taken as conceptually and empirically crucial for a philosophical analysis of such states (...) This is the first monograph that focuses on the PNS and its constitutive role in sensory states, including pain, mechanoception, proprioception, tactile perception, and so forth. -/- The author argues that the brain-centeredness of current philosophy of mind is a prejudice, and proposes a series of original ways in which classic puzzles in the philosophy of mind can be solved once the hypothesis that PNS is a constitutive element of mental states is taken seriously. The author calls this “the Peripheral Mind Hypothesis”, and employs it in a vast range of issues, such as functionalism, physicalism, mental content, embodiment, as well as some issues in neuroethics. -/- Making equal use of conceptual analysis, empirical data from neuroscience, first-person phenomenological data, and philosophical speculation, this work offers a fresh look at, and novel solutions to many philosophical problems. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I (...) am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

Doctoral graduates in philosophy are an excellent source of information about the discipline: they are at the cutting edge of research trends, have an inside view of researchfocused departments, and their employment prospects provide early insights on the future health of the discipline. We report on the results of a survey sent to recent PhD graduates and current students, as well as data gathering efforts by Academic Placement Data and Analysis that have taken place over the (...) past ten years. In this report we especially focus on demographic representation, program ratings, academic job placement, and non-academic careers. (shrink)

La raccolta di dati nella nostra era dell’”Information Technology” ha generato una rivoluzione nella conoscenza. Nell’era dei “big data”, la conseguente crescita senza precedenti dei dati, ha reso necessari cambiamenti nella scala, nella natura e nello stato dei dati, portando quindi i ricercatori ad adottare nuovi paradigmi e metodologie nella ricerca filosofica. In particolare, l’attenzione teorica della filosofia del linguaggio si è spostata verso la conoscenza cognitiva, con un’enfasi sulla proposizione particolare del “data turn” nella cognizione cognitiva nell’era (...) dei “big data”. Il paper esplora la potenziale portata della ricerca quantitativa del “data turn” nella filosofia del linguaggio, tramite l’analisi della necessità di trasformare i paradigmi della ricerca qualitativa e quantitativa, ricostruendo l’approccio quantitativo della filosofia del linguaggio ed ampliando l’analisi delle relazioni uomo-dati nella filosofia dei “big data”. Il paper conclude affermando che sono necessarie ulteriori ricerche per esaminare in modo ancor più approfondito la relazione tra linguaggio, dati e filosofia. (shrink)

This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to (...) the current theory; or (7) change the theory. We analyze psychological experiments and cases from the history of science to support this proposal. Implications for the philosophy of science are discussed. (shrink)

The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data (...) science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further (...) analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding. (shrink)

This article considers the temporal dimension of data processing and use and the ways in which it affects the production and interpretation of knowledge claims. I start by distinguishing the time at which data collection, dissemination, and analysis occur from the time in which the phenomena for which data serve as evidence operate. Building on the analysis of two examples of data reuse from modeling and experimental practices in biology, I then argue that Dt (...) affects how researchers select and interpret data as evidence and identify and understand phenomena. (shrink)

The essay proposes to use alienation as a heuristic and conceptual tool for the analysis of the impact of digital information and communication technologies (ICTs) on users. It follows a historical materialist understanding, according to which data can be considered as things produced in an industrial fashion. A representational interpretation, according to which data would merely reflect a given reality, is untenable. It will be argued instead to understand data as an additional layer which has a (...) transformative impact on reality as a whole. The analysis follows the early Marx in thinking of alienation as a structural condition which holds even if it is not experienced psychologically by users. Alienation means that the desire to express oneself and achieve social recognition, self-realization, and participation through the use of ICTs is incongruent to the inherent purposes and functions of these technologies. Digital platforms create the ideological illusion that users contribute to the production of data solely for their own gratification. Users, in turn, react to structural alienation either by internalizing it, in which case they may become enthusiastic data producers, or by imposing alienating conditions on other users. Finally, it will be shown that a strong notion of privacy is less suited to articulate concerns about the impact of ICTs because it expresses a desire for privilege and individual control which has become unfulfillable in the environment of data industries. The essay closes by showing some of the changes in institutional structures and behavior that could follow from the focus on alienation. (shrink)

A qualitative study was conducted to examine the philosophy of technology of K-12 technology leaders, and explore the influence of their thinking on technology decision making. The research design aligned with CORBIN and STRAUSS grounded theory methods, and I proceeded from a research paradigm of critical realism. The subjects were school technology directors and instructional technology specialists, and data collection consisted of interviews and a written questionnaire. Data analysis involved the use of grounded theory methods including (...) memo writing, open and axial coding, constant comparison, the use of purposive and theoretical sampling, and theoretical saturation of categories. Three broad philosophy of technology views were widely held by participants: an instrumental view of technology, technological optimism, and a technological determinist perspective that saw technological change as inevitable. Technology leaders were guided by two main approaches to technology decision making, represented by the categories Educational goals and curriculum should drive technology, and Keep up with Technology (or be left behind). The core category and central phenomenon that emerged was that technology leaders approached technology leadership by placing greater emphasis on keeping up with technology, being influenced by an ideological orientation to technological change, and being concerned about preparing students for a technological future. (shrink)

The Philosophy of Quantitative Methods undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.

To make sense of large data sets, we often look for patterns in how data points are “shaped” in the space of possible measurement outcomes. The emerging field of topological data analysis offers a toolkit for formalizing the process of identifying such shapes. This article aims to discover why and how the resulting analysis should be understood as reflecting significant features of the systems that generated the data. I argue that a particular feature of (...) TDA—its functoriality—is what enables TDA to translate visual intuitions about structure in data into precise, computationally tractable descriptions of real-world systems. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate (...) model for facilitating this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

By means of a citation analysis I aim to determine which scholarly journals are most important in the sub-field of philosophy of science. My analysis shows that the six most important journals in the sub-field are Philosophy of Science , British Journal for the Philosophy of Science , Journal of Philosophy , Synthese , Studies in History and Philosophy of Science , and Erkenntnis . Given the data presented in this study, there (...) is little evidence that there is such a field as the history and philosophy of science (HPS). Rather, philosophy of science is most properly conceived of as a sub-field of philosophy. (shrink)

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state (...) of digital work in the philosophy of science, showing what kinds of questions can be answered and how one can go about answering them. (shrink)

In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of (...) science, namely within the philosophical debate on modeling. Specifically, I review my empirical investigations into the issues of model de-idealization, model justification and performativity. (shrink)

Banking firms are becoming increasingly aware that their clients’ management of environmental and social risks may in term threaten their own business as lenders and investors. In addition, stakeholders are requiring banks to improve their social performance. As a result, some banks are developing corporate social responsibility (CSR) policies and management systems to reduce potential risks and improve their performance. In the Spanish financial system, half of the banking firms are savings banks, most of which have always used some Corporate (...) Social Responsibility (CSR) criteria in their management. Private Banks have only recently started to integrate social aspects in their performance. However, no formal analysis has been carried out on the impact of CSR strategies. Various initiatives have been launched nationally and internationally to include the social dimension in management systems. The purpose of this research is to analyse the social performance of the main Spanish financial companies through public data such as social or sustainability reports and media sources. In order to do this, we need to determine which CSR criteria most greatly affect banking firms and to choose the most accurate quantitative and qualitative indicators to measure social performance. (shrink)

The analysis of video-recorded interaction consists of various professionalized ways of seeing participant behavior through multimodal, co-operative, or intercorporeal lenses. While these perspectives are often adopted simultaneously, each creates a different view of the human body and interaction. Moreover, microanalysis is often produced through local practices of sense-making that involve the researchers’ bodies. It has not been fully elaborated by previous research how adopting these different ways of seeing human behavior influences both what is seen from a video and (...) how it is seen, as well as the way the interpretation of the data ultimately unfolds in the interaction between researchers. In this article, we provide a theoretical-methodological discussion of the microanalytic research process. We explore how it differs from “seeing” affect in interaction either as a co-operative and multimodal action or as an intercorporeal experience. First, we introduce the multimodal conversation analytic, co-operative, and intercorporeal approaches to microanalysis. Second, we apply and compare these practices to a video-recorded interaction of a romantic couple. Furthermore, we examine a video-recorded episode of us, the researchers, reflecting on our analytic observations about this interaction. We suggest that adopting a multimodal and co-operative perspective constructs affect as co-produced and displayed through observable action, while an intercorporeal perspective produces affect as an embodied and experienced phenomenon. While the former enables locating affect in a specific moment and identifiable body parts, the latter facilitates recognizing the experienced side of affect. These different modes of professional vision complement one another in capturing affect in interaction while being fundamentally used in local interactions between the researchers. (shrink)

Scholars have emphasized that decisions about technology can be influenced by philosophy of technology assumptions, and have argued for research that critically questions technological determinist assumptions. Empirical studies of technology management in fields other than K-12 education provided evidence that philosophy of technology assumptions, including technological determinism, can influence the practice of technology leadership. A qualitative study was conducted to a) examine what philosophy of technology assumptions are present in the thinking of K-12 technology leaders, b) investigate (...) how the assumptions may influence technology decision making, and c) explore whether technological determinist assumptions are present. The research design aligned with Corbin and Strauss qualitative data analysis, and employed constant comparative analysis, theoretical sampling, and theoretical saturation of categories. Subjects involved 31 technology directors and instructional technology specialists from Virginia school districts, and data collection involved interviews following a semi-structured protocol, and a written questionnaire with open-ended questions. The study found that three broad philosophy of technology views were widely held by participants, including an instrumental view of technology, technological optimism, and a technological determinist perspective that sees technological change as inevitable. The core category and central phenomenon that emerged was that technology leaders approach technology leadership through a practice of Keep up with technology (or be left behind). The core category had two main properties that are in conflict with each other, pressure to keep up with technology, and the resistance to technological change they encounter in schools. The study found that technology leaders are guided by two main approaches to technology decision making, represented by the categories Educational goals and curriculum should drive technology, and Keep up with Technology (or be left behind). As leaders deal with their perceived experience of the inevitability of technological change, and their concern for preparing students for a technological future, the core category Keep up with technology (or be left behind) is given the greater weight in technology decision making. The researcher recommends that similar qualitative studies be conducted involving technology leaders outside Virginia, and with other types of educators. It is also recommended that data from this or other qualitative studies be used to help develop and validate a quantitative instrument to measure philosophy of technology assumptions, for use in quantitative studies. (shrink)

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. 'Biomedical Big Data' refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that (...) can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward. (shrink)

Philosophy of dialectical materialism and logic.

The introduction of genomics and biobanking methodologies to the African research context has also introduced novel ways of doing science, based on values of sharing and reuse of data and samples. This shift raises ethical challenges that need to be considered when research is reviewed by ethics committees, relating for instance to broad consent, the feedback of individual genetic findings, and regulation of secondary sample access and use. Yet existing ethics guidelines and regulations in Africa do not successfully regulate (...) research based on sharing, causing confusion about what is allowed, where and when. In order to understand better the ethics regulatory landscape around genomic research and biobanking, we conducted a comprehensive analysis of existing ethics guidelines, policies and other similar sources. We sourced 30 ethics regulatory documents from 22 African countries. We used software that assists with qualitative data analysis to conduct a thematic analysis of these documents. Surprisingly considering how contentious broad consent is in Africa, we found that most countries allow the use of this consent model, with its use banned in only three of the countries we investigated. In a likely response to fears about exploitation, the export of samples outside of the continent is strictly regulated, sometimes in conjunction with regulations around international collaboration. We also found that whilst an essential and critical component of ensuring ethical best practice in genomics research relates to the governance framework that accompanies sample and data sharing, this was most sparingly covered in the guidelines. There is a need for ethics guidelines in African countries to be adapted to the changing science policy landscape, which increasingly supports principles of openness, storage, sharing and secondary use. Current guidelines are not pertinent to the ethical challenges that such a new orientation raises, and therefore fail to provide accurate guidance to ethics committees and researchers. (shrink)

Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to use. She examines (...) early mechanical models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes their roots in the mathematical principles of Newton and others, and compares them to contemporary mechanistic approaches. Bailer-Jones then views the use of analogy in the late nineteenth century as a means of understanding models and to link different branches of science. She reveals how analogies can also be models themselves, or can help to create them. The first half of the twentieth century saw little mention of models in the literature of logical empiricism. Focusing primarily on theory, logical empiricists believed that models were of temporary importance, flawed, and awaiting correction. The later contesting of logical empiricism, particularly the hypothetico-deductive account of theories, by philosophers such as Mary Hesse, sparked a renewed interest in the importance of models during the 1950s that continues to this day. Bailer-Jones analyzes subsequent propositions of: models as metaphors; Kuhn's concept of a paradigm; the Semantic View of theories; and the case study approaches of Cartwright and Morrison, among others. She then engages current debates on topics such as phenomena versus data, the distinctions between models and theories, the concepts of representation and realism, and the discerning of falsities in models. (shrink)

This paper presents new data on the representation of women who publish in 25 top philosophy journals as ranked by the Philosophical Gourmet Report for the years 2004, 2014, and 2015. It also provides a new analysis of Schwitzgebel’s 1955–2015 journal data. The paper makes four points while providing an overview of the current state of women authors in philosophy. In all years and for all journals, the percentage of female authors was extremely low, in (...) the range of 14–16%. The percentage of women authors is less than the percentage of women faculty in different ranks and at different kinds of institutions. In addition, there is great variation across individual journals, and the discrepancy between women authors and women faculty appears to be different in different subfields. Interestingly, journals which do not practice anonymous review seem to have a higher percentage of women authors than journals which practice double anonymous or triple anonymous review. This paper also argues that we need more data on academic publishing to better understand whether this can explain why there are so few full-time female faculty in philosophy, since full-time hiring and tenuring practices presumably depend on a candidate’s academic publishing. (shrink)

Background Informed consent is one of the key principles of conducting research involving humans. When research participants give consent, they perform an act in which they utter, write or otherwise provide an authorisation to somebody to do something. This paper proposes a new understanding of the informed consent as a compositional act. This conceptualisation departs from a modular conceptualisation of informed consent procedures. Methods This paper is a conceptual analysis that explores what consent is and what it does or (...) does not do. It presents a framework that explores the basic elements of consent and breaks it down into its component parts. It analyses the consent act by first identifying its basic elements, namely: a) data subjects or legal representative that provides the authorisation of consent; b) a specific thing that is being consented to; and c) specific agent(s) to whom the consent is given. Results This paper presents a framework that explores the basic elements of consent and breaks it down into its component parts. It goes beyond only providing choices to potential research participants; it explains the rationale of those choices or consenting acts that are taking place when speaking or writing an authorisation to do something to somebody. Conclusions We argue that by clearly differentiating the goals, the procedures of implementation, and what is being done or undone when one consent, one can better face the challenges of contemporary data-intensive biomedical research, particularly regarding the retention and use of data. Conceptualising consent as a compositional act enhances more efficient communication and accountability and, therefore, could enable more trustworthy acts of consent in biomedical science. (shrink)

Millions of scientific articles are published each year, making it difficult to stay abreast of advances within even the smallest subdisciplines. Traditional approaches to the study of science, such as the history and philosophy of science, involve closely reading a relatively small set of journal articles. And yet many questions benefit from casting a wider net: Is most scientific change gradual or revolutionary? What are the key sources of scientific novelty? Over the past several decades, a massive effort to (...) digitize the academic literature and equip computers with algorithms that can distantly read and analyze a digital database has taken us one step closer to answering these questions. The Dynamics of Science brings together a diverse array of contributors to examine the largely unexplored computational frontiers of history and philosophy of science. Together, they reveal how tools and data from automated textual analysis, or machine "reading," combined with methods and models from game theory and cultural evolutionary theory, can begin to answer fundamental questions about the nature and history of science. (shrink)

This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`.

The unprecedented development of medical informatics is constantly transforming the concept of expertise in medical sciences in a way that has far-reaching consequences for both the theory of knowledge and the philosophy of informatics. Deep medicine is based on the assumption that medical diagnosis should take into account the wide array of possible health factors involved in the diagnostic process, such as not only genome analysis alone, but also the metabolome (analysis of all body metabolites important for (...) e.g. drug-drug interactions), microbiome (i.e. analysis of all bodily microorganisms interacting with host cells) or exposome (analysis of all environmental factors triggering potentially harmful cell mutations, such as UV radiation, heavy metals, various carcinogens, etc.). Deep data analysis is of tantamount importance for personalized diagnosis but, at the same time, hardly achievable by a regular human being. However, adequately designed artificial intelligence (e.g. a deep neural network) can undeniably be of great help for finding correlations between symptoms and underlying diseases. Nowadays AI applies to nearly every aspect of medicine, starting from the data analysis of scientific literature, through the diagnostic process, to the act of communication between physicians and their patients. Medical image processing algorithms greatly enhance the chances of successful recognition of melanoma or intestinal polyps. Communication tools designed for physicians use techniques known from social media to provide users with an opportunity to consult the case with colleagues from the same discipline. Natural language processing tools significantly improve doctor-patient communication by the automation of note-taking. Is this enough to support the claim that the non-epistemic competences in medicine are becoming more and more important? Can we attribute expertise only to a person? How is medical informatics changing the way most people usually understand human-computer interactions? (shrink)

The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that (...) tradition hasn’t received the wider attention it so thoroughly deserves. This review will briefly introduce some recent significant topics of debate within the philosophy of biology, focusing on those whose metaphysical themes (in everything from composition to causation) are likely to be of wide-reaching, cross-disciplinary interest. (shrink)

The standard of disinterested objectivity embedded within the US Data Quality Act (2001) has been used by corporate and political interests as a way to limit the dissemination of scientific research results that conflict with their goals. This is an issue that philosophers of science can, and should, publicly address because it involves an evaluation of the strength and adequacy of evidence. Analysis of arguments from a philosophical tradition that defended a concept of useful knowledge (later displaced by (...) Logical Empiricism) is used here to suggest how the legitimacy of scientific findings can be supported in the absence of disinterested objectivity. (shrink)

This article describes the effect of a teaching–learning sequence based on the discovery of oxygen in promoting students’ argumentation. It examines the written and oral arguments produced by 63 high school students in France during a complete TLS supervised by the same teacher. The data used in this analysis was derived from students’ written responses, audio and video recordings, and written field notes. The first goal of this investigation was to provide evidence that an approach combining history and (...) philosophy of science and argumentation could increase students’ awareness of the relevance of experimentation and communication to scientific progress. The second goal was to assess the effectiveness of the TLS to engage students in argumentative classroom interactions relating to the discovery of oxygen at the end of the 18th century. The findings show that this historical case can be useful for promoting students’ argumentation and is also appropriate for high school students. Future research should include students of other ages, other historical episodes and experiences in other parts of the world. (shrink)

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

Context: The use of first-person micro-phenomenological interviews and their productive interaction with third-person physiological data is a challenging and pressing issue in order to offer an effective and fruitful application of Varela’s neurophenomenological hypothesis. Problem: We aim at offering a generative method of analysis of first-person micro-phenomenological interviews using third-person physiological data. Our challenge is to describe this generative first-person analysis with the third-person physiological framework rather than put Varela’s hypothesis into practice in a generative way (...) (as we did in another paper. Method: The present contribution is a first pioneering study as far as the exposition of such an interactive generative methodology is concerned. It is also a new issue insofar as it deals with a case study, surprise in depression, that has not been thoroughly dealt with so far, either in philosophy or in psychopathology. Results: We show that the analysis of first-person data is an intrinsic generative one, insofar as new refined categories and multifarious circular micro- and macro-processes were discovered in the very process of analyzing. They provide the initial structural generic third-person description of surprise inherited both from philosophical phenomenological a priori categories and from the experimental startle setting with a refined micro-segmentation of the dynamic of the experience. Implications: Our article could be of interest to neurophenomenologists looking for an effective application and to researchers in quest of a method of analysis of first-person data. The present limitations are due to the still preliminary data-results we need to complete. Constructivist content: The article is directly linked to Varela’s neurophenomenological program and aims at extending and reforming it with a cardio-phenomenological approach. Keywords: First-person micro-phenomenological interviews, surprise, generative analysis of first-person data, depression, cardio-phenomenology, generative categories. (shrink)

What is data? That question is the fundamental investigation of this dissertation. I have developed a methodology from social-scientific processes to explore how different people understand the concept of data, rather than to rely on my own philosophical intuitions or thought experiments about the “nature” of data. The evidence I have gathered as to different individuals' constructions of data can be used to inform further inquiry of data and the design of information systems. My research (...) demonstrates that people have different constructions of data. The methodology of the SDFN, created for this dissertation, has proven able to probe those understandings. The SDFN, loosely based on a DFD and combined with ideas from SNA, provides a way of discovering practical definitions of hard-to-operationalize terms like data. The process of repeatedly categorizing various items as data allows the methodology to explore how participants actually use the term, rather than relying on theoretical dictionary-based definitions. Analysis of the interviews found three different constructions of data: data as communications, a container for meaning; data as subjective observations, sense-impressions filtered by knowledge; and data as objective facts, measurements revealing the relationships of reality. (shrink)

How might philosophy of religion be impacted by developments in computational modeling and social simulation? After briefly describing some of the content and context biases that have shaped traditional philosophy of religion, this article provides examples of computational models that illustrate the explanatory power of conceptually clear and empirically validated causal architectures informed by the bio-cultural sciences. It also outlines some of the material implications of these developments for broader metaphysical and metaethical discussions in philosophy. Computer modeling (...) and simulation can contribute to the reformation of the philosophy of religion in at least three ways: by facilitating conceptual clarity about the role of biases in the emergence and maintenance of phenomena commonly deemed “religious,” by supplying tools that enhance our capacity to link philosophical analysis and synthesis to empirical data in the psychological and social sciences, and by providing material insights for metaphysical hypotheses and metaethical proposals that rely solely on immanent resources. (shrink)

Did Chinese mysticism vanish after its first appearance in ancient Taoist philosophy, to surface only after a thousand years had passed, when the Chinese had adapted Buddhism to their own culture? This first integrated survey of the mystical dimension of Taoism disputes the commonly accepted idea of such a hiatus. Covering the period from the Daode jing to the end of the Tang, Livia Kohn reveals an often misunderstood Chinese mystical tradition that continued through the ages. Influenced by but (...) ultimately independent of Buddhism, it took forms more various than the quietistic withdrawal of Laozi or the sudden enlightenment of the Chan Buddhists. On the basis of a new theoretical evaluation of mysticism, this study analyzes the relationship between philosophical and religious Taoism and between Buddhism and the native Chinese tradition. Kohn shows how the quietistic and socially oriented Daode jing was combined with the ecstatic and individualistic mysticism of the Zhuangzi, with immortality beliefs and practices, and with Buddhist insight meditation, mind analysis, and doctrines of karma and retribution. She goes on to demonstrate that Chinese mysticism, a complex synthesis by the late Six Dynasties, reached its zenith in the Tang, laying the foundations for later developments in the Song traditions of Inner Alchemy, Chan Buddhism, and Neo-Confucianism. (shrink)

In this paper I review the Neo-Davidsonian semantics of prepositional phrases and secondary predication. I argue that certain types of examples pose challenge to this semantics. I present an alternative to the Neo-Davidsonian analysis which successfully deals with the problematic examples. The core idea lies in representing theta-roles not as functions from events to their participants, but rather as argument-labels encoding the role of each argument in a given verb. As a result, natural-language predicates can now be treated in (...) the manner in which relations are treated in the relational model of data, that is, as naming sets of tuples in which every object is given together with its role named by a corresponding attribute (a theta-role). Such a representation allows the employment of relational algebra operators to calculate the extensions of complex predicates (predicates built out of atomic predicates and/or atomic predicates and prepositions). I lay out the foundations of a relational FOL appropriate for the representation of natural-language predicates and present solutions to the problematic examples. From a practical perspective, the expressions of a relational FOL can be translated to a relational algebra or SQL, which makes it possible to operate with these three languages on the same relational model of data. From a philosophical perspective, a relational FOL permits a return to ‘property-based’ semantics, one in which properties named by predicates are those of individuals, and not of events. (shrink)