80 found
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  1. Interpreting causality in the health sciences.Federica Russo & Jon Williamson - 2007 - International Studies in the Philosophy of Science 21 (2):157 – 170.
    We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory (...)
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  2. Mechanisms and the Evidence Hierarchy.Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson - 2014 - Topoi 33 (2):339-360.
    Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in (...)
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  3.  89
    Evaluating evidence of mechanisms in medicine.Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw & Jon Williamson - 2018 - Dordrecht, Netherlands: Springer. Edited by Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw, Christian Wallmann, Michael Wilde & Jon Williamson.
    The use of evidence in medicine is something we should continuously seek to improve. This book seeks to develop our understanding of evidence of mechanism in evaluating evidence in medicine, public health, and social care; and also offers tools to help implement improved assessment of evidence of mechanism in practice. In this way, the book offers a bridge between more theoretical and conceptual insights and worries about evidence of mechanism and practical means to fit the results into evidence assessment procedures.
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  4. Causality: Philosophical theory meets scientific practice.Phyllis McKay Illari & Federica Russo - 2014 - Oxford, UK: Oxford University Press. Edited by Federica Russo.
    Scientific and philosophical literature on causality has become highly specialised. It is hard to find suitable access points for students, young researchers, or professionals outside this domain. This book provides a guide to the complex literature, explains the scientific problems of causality and the philosophical tools needed to address them.
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  5.  65
    Critical data studies: An introduction.Federica Russo & Andrew Iliadis - 2016 - Big Data and Society 3 (2).
    Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...)
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  6. Causality and causal modelling in the social sciences.Federica Russo - 2009 - Springer, Dordrecht.
    The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...)
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  7.  99
    The Evidence that Evidence-based Medicine Omits.Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson - unknown
    According to current hierarchies of evidence for EBM, evidence of correlation is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, (...)
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  8. Epistemic causality and evidence-based medicine.Federica Russo & Jon Williamson - 2011 - History and Philosophy of the Life Sciences 33 (4).
    Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which (...)
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  9.  24
    Causality in the Sciences.Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.) - 2011 - New York: Oxford University Press.
    Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.
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  10.  26
    Science and values: a two-way direction.Emanuele Ratti & Federica Russo - 2024 - European Journal for Philosophy of Science 14 (1):1-23.
    In the science and values literature, scholars have shown how science is influenced and shaped by values, often in opposition to the ‘value free’ ideal of science. In this paper, we aim to contribute to the science and values literature by showing that the relation between science and values flows not only from values into scientific practice, but also from (allegedly neutral) science to values themselves. The extant literature in the ‘science and values’ field focuses by and large on reconstructing, (...)
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  11.  57
    Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Lorenzo Casini, Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - Theoria 26 (1):5-33.
    The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how (...)
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  12. Philosophy of science in practice in ecological model building.Luana Poliseli, Jeferson G. E. Coutinho, Blandina Viana, Federica Russo & Charbel N. El-Hani - 2022 - Biology and Philosophy 37 (4):0-0.
    This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstruction and understanding of mechanisms is context-sensitive, and for this case study mechanistic modeling did not present a normative role but a heuristic one. We expect our study to provides useful epistemic tools for (...)
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  13.  39
    Reconstructing the mixed mechanisms of health: the role of bio- and socio-markers.Virginia Ghiara & Federica Russo - unknown
    It is widely agreed that social factors are related to health outcomes: much research served to establish correlations between classes of social factors on the one hand and classes of disease on the other hand. However, why and how social factors are an active part in the aetiology of disease development is something that is gaining attention only recently in the health sciences and in the medical humanities. In this paper, we advance the view that, just as bio-markers help trace (...)
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  14.  82
    Information Channels and Biomarkers of Disease.Phyllis Illari & Federica Russo - 2016 - Topoi 35 (1):175-190.
    Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research in causality has (...)
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  15. EnviroGenomarkers: The Interplay Between Mechanisms and Difference Making in Establishing Causal Claims.Federica Russo & Jon Williamson - 2012 - Medicine Studies 3 (4):249-262.
    According to Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1(1):47–69, 2011b ), in order to establish a causal claim of the form, ‘_C_ is a cause of _E_’, one typically needs evidence that there is an underlying mechanism between _C_ and _E_ as well as evidence that _C_ makes a difference to _E_. This thesis has been used to argue that hierarchies of evidence, as championed by evidence-based movements, tend to (...)
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  16.  83
    Causal models and evidential pluralism in econometrics.Alessio Moneta & Federica Russo - 2014 - Journal of Economic Methodology 21 (1):54-76.
    Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on the basis of a (...)
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  17.  74
    Connecting ethics and epistemology of AI.Federica Russo, Eric Schliesser & Jean Wagemans - forthcoming - AI and Society:1-19.
    The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and (...)
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  18.  57
    Epistemic Diversity and the Question of Lingua Franca in Science and Philosophy.Federico Gobbo & Federica Russo - 2020 - Foundations of Science 25 (1):185-207.
    Epistemic diversity is the ability or possibility of producing diverse and rich epistemic apparati to make sense of the world around us. In this paper we discuss whether, and to what extent, different conceptions of knowledge—notably as ‘justified true belief’ and as ‘distributed and embodied cognition’—hinder or foster epistemic diversity. We then link this discussion to the widespread move in science and philosophy towards monolingual disciplinary environments. We argue that English, despite all appearance, is no Lingua Franca, and we give (...)
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  19. Why look at Causality in the Sciences?Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
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  20. Variational Causal Claims in Epidemiology.Federica Russo - 2009 - Perspectives in Biology and Medicine 52 (4):540-554.
    The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact (...)
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  21. Correlational Data, Causal Hypotheses, and Validity.Federica Russo - 2011 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 42 (1):85 - 107.
    A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a 'structural strategy', the goal of which (...)
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  22.  30
    Causal Explanation: Recursive Decompositions and Mechanisms.Michel Mouchart & Federica Russo - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
  23. The Rationale of Variation in Methodological and Evidential Pluralism.Federica Russo - 2006 - Philosophica 77 (1).
    Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...)
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  24.  45
    Causality in Cancer Research: a Journey Through Models in Molecular Epidemiology and their Philosophical Interpretation.Paolo Vineis, Phyllis Illari & Federica Russo - 2017 - Emerging Themes in Epidemiology 14 (7):1-8.
    In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making diferent traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, the “sufcient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics (...)
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  25.  42
    COVID-19 heralds a new epistemology of science for the public good.Manfred D. Laubichler, Peter Schlosser, Jürgen Renn, Federica Russo, Gerald Steiner, Eva Schernhammer, Carlo Jaeger & Guido Caniglia - 2021 - History and Philosophy of the Life Sciences 43 (2):1-6.
    COVID-19 has revealed that science needs to learn how to better deal with the irreducible uncertainty that comes with global systemic risks as well as with the social responsibility of science towards the public good. Further developing the epistemological principles of new theories and experimental practices, alternative investigative pathways and communication, and diverse voices can be an important contribution of history and philosophy of science and of science studies to ongoing transformations of the scientific enterprise.
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  26.  30
    On the Poietic Character of Technology.Federica Russo - 2016 - Humana Mente 9 (30).
    Large part of contemporary science is in fact technoscience, in the sense that it crucially depends on several technologies for the generation, collection, and analysis of data. This prompts a re-examination of the relations between science and technologies. In this essay, I advance the view that we’d better move beyond the ‘subordination view’ and the ‘instrumental’ view. The first aims to establish the primacy of science over technology, and the second uses technology instrumentally to support a realist position about theoretical (...)
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  27. Structural Modelling, Exogeneity, and Causality.Federica Russo, Michel Mouchart & Guillaume Wunsch - 2009 - In Federica Russo, Michel Mouchart & Guillaume Wunsch (eds.), Causal Analysis in Population Studies. pp. 59-82.
    This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of (...)
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  28. Public health policy, evidence, and causation: lessons from the studies on obesity.Federica Russo - 2012 - Medicine, Health Care and Philosophy 15 (2):141-151.
    The paper addresses the question of how different types of evidence ought to inform public health policy. By analysing case studies on obesity, the paper draws lessons about the different roles that different types of evidence play in setting up public health policies. More specifically, it is argued that evidence of difference-making supports considerations about ‘what works for whom in what circumstances’, and that evidence of mechanisms provides information about the ‘causal pathways’ to intervene upon.
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  29.  30
    Digital Technologies, Ethical Questions, and the Need of an Informational Framework.Federica Russo - 2018 - Philosophy and Technology 31 (4):655-667.
    Technologies have always been bearers of profound changes in science, society, and any other aspect of life. The latest technological revolution—the digital revolution—is no exception in this respect. This paper presents the revolution brought about by digital technologies through the lenses of a specific approach: the philosophy of information. It is argued that the adoption of an informational approach helps avoiding utopian or dystopian approaches to technology, both expressions of technological determinism. Such an approach provides a conceptual framework able to (...)
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  30.  55
    What Invariance Is and How to Test for It.Federica Russo - 2014 - International Studies in the Philosophy of Science 28 (2):157-183.
    Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...)
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  31.  31
    Causality and Probability in the Sciences.Federica Russo & Jon Williamson (eds.) - 2007 - College Publications.
    Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics to infer causal relationships. The social and health sciences analyse population-level data using statistical methods to infer average causal relations. In diagnosis of disease, probabilistic statements are based on population-level causal knowledge combined with knowledge of a particular person’s symptoms. For the physical sciences, the Salmon-Dowe account develops an (...)
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  32.  16
    Experimental practices and objectivity in the social sciences: re-embedding construct validity in the internal–external validity distinction.María Jiménez-Buedo & Federica Russo - 2021 - Synthese 199 (3-4):9549-9579.
    The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the biomedical sciences, and that allow for the adjudication among contentious causal claims. We examine (...)
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  33.  93
    Causal webs in epidemiology.Federica Russo - unknown
    The notion of ‘causal web’ emerged in the epidemiological literature in the early Sixties and had to wait until the Nineties for a thorough critical appraisal. Famously, Nancy Krieger argued that such a notion isn’t helpful unless we specify what kind of spiders create the webs. This means, according to Krieger, (i) that the role of the spiders is to provide an explanation of the yarns of the web and (ii) that the sought spiders have to be biological and social. (...)
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  34.  62
    On Empirical Generalisations.Federica Russo - 2012 - In Dennis Dieks, Wenceslao J. Gonzalez, Stephan Hartmann, Michael Stöltzner & Marcel Weber (eds.), Probabilities, Laws, and Structures. Springer. pp. 123-139.
    Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the relationship between X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called 'invariant empirical generalisations'. The paper focuses on questions about causal assessment (...)
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  35.  82
    Are causal analysis and system analysis compatible approaches?Federica Russo - 2010 - International Studies in the Philosophy of Science 24 (1):67 – 90.
    In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal analysis can be integrated into the (...)
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  36.  59
    Functions and Mechanisms in Structural-Modelling Explanations.Guillaume Wunsch, Michel Mouchart & Federica Russo - 2014 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 45 (1):187-208.
    One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...)
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  37. Where to after COVID-19? Systems thinking for a human-centred approach to pandemics.Maru Mormina, Bernhard Müller, Guido Caniglia, Eivind Engebretsen, Henriette Löffler-Stastka, James Marcum, Mathew Mercuri, Elisabeth Paul, Holger Pfaff, Federica Russo, Joachim Sturmberg, Felix Tretter & Wolfram Weckwerth - unknown
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  38.  36
    Introduction.Phyllis Illari, Julian Reiss & Federica Russo - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):758-760.
  39. Philosophy of science in practice and weak scientism together apart.Luana Poliseli & Federica Russo - 2022 - In Moti Mizrahi Mizrahi (ed.), For and Against Scientism: Science, Methodology, and the Future of Philosophy. Lanham: Rowman & Littlefield Publishers. pp. 0-0.
    The term ‘scientism’ has not attracted consensus about its meaning or about its scope of application. In this paper, we consider Mizrahi’s suggestion to distinguish ‘Strong’ and ‘Weak’ scientism, and the consequences this distinction may have for philosophical methodology. While we side with Mizrahi that his definitions help advance the debate, by avoiding verbal dispute and focussing on questions of method, we also have concerns about his proposal as it defends a hierarchy of knowledge production. Mizrahi’s position is that Weak (...)
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  40.  10
    How is who: evidence as clues for action in participatory sustainability science and public health research.Guido Caniglia & Federica Russo - 2024 - History and Philosophy of the Life Sciences 46 (1):1-26.
    Participatory and collaborative approaches in sustainability science and public health research contribute to co-producing evidence that can support interventions by involving diverse societal actors that range from individual citizens to entire communities. However, existing philosophical accounts of evidence are not adequate to deal with the kind of evidence generated and used in such approaches. In this paper, we present an account of evidence as clues for action through participatory and collaborative research inspired by philosopher Susan Haack’s theory of evidence. Differently (...)
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  41. Interpreting probability in causal models for cancer.Federica Russo & Jon Williamson - 2007 - In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. pp. 217--242.
    How should probabilities be interpreted in causal models in the social and health sciences? In this paper we take a step towards answering this question by investigating the case of cancer in epidemiology and arguing that the objective Bayesian interpretation is most appropriate in this domain.
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  42.  51
    Scientific Disagreement and Evidential Pluralism: Lessons from the Studies on Hypercholesterolemia.Veli-Pekka Parkkinen, Federica Russo & Christian Wallmann - 2017 - Humana Mente 10 (32):75-116.
    Inconsistencies between scientific theories have been studied, by and large, from the perspective of paraconsistent logic. This approach considered the formal properties of theories and the structure of inferences one can legitimately draw from theories. However, inconsistencies can be also analysed from the perspective of modelling practices, in particular how modelling practices may lead scientists to form opinions and attitudes that are different, but not necessarily inconsistent. In such cases, it is preferable to talk about disagreement, rather than inconsistency. Disagreement (...)
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  43.  12
    Interpretations of probability.Jon Williamson & Federica Russo - 2010 - In Jon Williamson & Federica Russo (eds.), Key Terms in Logic. pp. 81.
    Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...)
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  44.  12
    Predicting “it will work for us”: (way) beyond statistics.Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences.
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  45. On The Foundations Of Agency-Manipulability Theories Of Causation.Federica Russo - unknown
    The Agency and the Manipulability theory of causation, in spite of significant differences, share at least three claims. First, that manipulation – roughly, that by manipulating causes we bring about effects – is a central notion for causation; second, that such a notion of manipulation allows a reductive – i.e. general and comprehensive – account of causation; third, that this view has its forefathers in the works of Collingwood, Gasking and von Wright. This paper mainly challenges the third claim and (...)
     
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  46.  37
    Combining Probability and Logic.Fabio Cozman, Rolf Haenni, Jan-Willem Romeijn, Federica Russo, Gregory Wheeler & Jon Williamson - 2009 - Journal of Applied Logic 7 (2):131-135.
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  47. Assessing Exposures.Jon Williamson, Beth Shaw, Federica Russo, Charles Norell, Michael Kelly, Phyllis Illari, Brendan Clarke, Michael Wilde, Christian Wallmann & Veli-Pekka Parkkinen - 2018 - In Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw & Jon Williamson (eds.), Evaluating evidence of mechanisms in medicine. Dordrecht, Netherlands: Springer.
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  48.  4
    Bayesianism.Jon Williamson & Federica Russo - 2010 - In Jon Williamson & Federica Russo (eds.), Key Terms in Logic. pp. 27.
    Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students and assuming no (...)
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  49. How to Consider Evidence of Mechanisms: An Overview.Jon Williamson, Beth Shaw, Federica Russo, Charles Norell, Michael Kelly, Phyllis Illari, Brendan Clarke, Michael Wilde, Christian Wallmann & Veli-Pekka Parkkinen - 2018 - In Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw & Jon Williamson (eds.), Evaluating evidence of mechanisms in medicine. Dordrecht, Netherlands: Springer.
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  50. Introduction.Jon Williamson, Beth Shaw, Federica Russo, Charles Norell, Michael Kelly, Phyllis Illari, Brendan Clarke, Michael Wilde, Christian Wallmann & Veli-Pekka Parkkinen - 2018 - In Veli-Pekka Parkkinen, Christian Wallmann, Michael Wilde, Brendan Clarke, Phyllis Illari, Michael P. Kelly, Charles Norell, Federica Russo, Beth Shaw & Jon Williamson (eds.), Evaluating evidence of mechanisms in medicine. Dordrecht, Netherlands: Springer.
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