This book is unique in presenting an interdisciplinary conversation between jurists and logicians. It brings together scholars from both law and philosophy and looks at the application of 'the new logics' to law and legal ordering, in a number of legal systems. The first Part explores the ways in which the new logics shed light on the functioning of legal orders, including the structure of legal argumentation and the rules of evidence. The second addresses how non-classical logics can help us (...) to understand the interactions between multiple legal orders, in a range of contexts including domestic and international law. The final Part examines particular issues in the applicability of non-classical logics to legal reasoning. This book will be of interest to jurisprudence and logic scholars and students who want to deepen their understanding of relationships between law and legal reasoning, and learn about recent developments in formal logic. (shrink)

One of the most serious theoretical obstacles to contemporary spacetime substantivalism is Earman and Norton's hole argument. We argue that applying the bundle theory of substance to spacetime points allows spacetime substantivalists to escape the conclusion of this argument. Some philosophers have claimed that the bundle theory cannot be applied to substantival spacetime in this way due to problems in individuating spacetime points in symmetrical spacetimes. We demonstrate that it is possible to overcome these difficulties if spatiotemporal properties are viewed (...) as tropes rather than universals. (shrink)

School counselors encounter ethical and legal situations that necessitate the knowledge and confidence to apply decision-making skills. We report the findings from a correlational investigation that examines practicing school counselors’ ethical and legal self-efficacy, ethical and legal knowledge, and general self-efficacy. Higher ethical and legal self-efficacy was associated with higher general self-efficacy and ethical and legal knowledge. The implications of these findings are discussed.

During Hallowe'en of 1970, the Department of Philosophy of the Univer sity of Western Ontario held its annual fall colloquium at London, On tario. The general topic of the sessions that year was conceptual change. The thirteen papers composing this volume stem more or less directly from those meetings; six of them are printed here virtually as delivered, while the remaining seven were subsequently written by invitation. The programme of the colloquium was to have consisted of major papers delivered by (...) Professors Wilfrid Sellars, Stephan Korner, Paul Ziff and Hilary Putnam, with shorter commentary thereupon by Professors Robert Binkley, Joseph Ullian, Jerry Fodor and Robert Barrett, respec tively. And that is the way it happened, with one important exception: at the eleventh hour, Sellars and Binkley exchanged roles. This gave Binkley the rather unusual and challenging task of providing a suitable Sellarsian answer to a question not of his own asking - for Binkley's paper was written under Sellars' original title. Sellars' own contribution to the vo lume is perhaps more nearly what he would have presented as main speaker than a direct response to Binkley. However, it has seemed best, on balance, to attempt no further stylistic accommodation of the one paper to the other; their mutual philosophical relevance will be evident in any case. The editors would here like to extend special thanks to both Sellars and Binkley for their extraordinary efforts under the circumstances. (shrink)

Several scholars have recognized the limitations of theories of moral reasoning in explaining moral behavior. They have argued that moral behavior may also be influenced by moral identity, or how central morality is to one’s sense of self. This idea has been supported by findings that people who exemplify moral behavior tend to place more importance on moral traits when defining their self-concepts (Colby & Damon, 1995). This paper takes the next step of examining individual variation in a construct highly (...) associated with immoral behavior — psychopathy. In Study 1, we test the hypothesis that individuals with a greater degree of psychopathic traits have a weaker moral identity. Within a large online sample, we found that individuals who scored higher on a measure of psychopathic traits were less likely to base their self-concepts on moral traits. In Study 2, we test whether this reduced sense of moral identity can be attributed to differences in moral judgment, which is another factor that could influence immoral behavior. Our results indicated that the reduced sense of moral identity among more psychopathic individuals was independent of variation in moral judgment. These results suggest that individuals with psychopathic traits may display immoral behavior partially because they do not construe their personal identities in moral terms. (shrink)

Drawing on original ethnobotanical and anthropological research among Indigenous peoples across the Amazon, we examine synergies and dissonances between Indigenous and Western scientific knowledge about the environment, resource use, and sustainability. By focusing on the sensory dimension of Indigenous engagements with the environment—an approach we have described as “sensory ecology” and explored through the method of “phytoethnography”—we promote a symmetrical dialogue between Indigenous and scientific understandings around such phenomena as animal–plant mutualisms, phytochemical toxicity, sustainable forest management in “multinatural” landscapes, and (...) the emergence of new diseases like the novel coronavirus SARS-CoV-2 (COVID-19). Drawing examples from our own and other published works, we explore the possibilities and limitations of a “parallax view” attempting to hold Indigenous and scientific knowledge in focus simultaneously. As the concept of “bioeconomy” emerges as a key alternative for sustainable development of the Amazon, we encourage a critical and urgent engagement between dominant Western conceptions and Indigenous Amazonian knowledge, practices, and cultural values. Cognitive science, which has long contributed to studies of Indigenous categorization and conceptualization of the natural world, continues to play an important role in building bridges of mutual communication and respect between Indigenous and scientific approaches to sustainability and biodiversity conservation. (shrink)

Peer review is a widely accepted instrument for raising the quality of science. Peer review limits the enormous unstructured influx of information and the sheer amount of dubious data, which in its absence would plunge science into chaos. In particular, peer review offers the benefit of eliminating papers that suffer from poor craftsmanship or methodological shortcomings, especially in the experimental sciences. However, we believe that peer review is not always appropriate for the evaluation of controversial hypothetical science. We argue that (...) the process of peer review can be prone to bias towards ideas that affirm the prior convictions of reviewers and against innovation and radical new ideas. Innovative hypotheses are thus highly vulnerable to being “filtered out” or made to accord with conventional wisdom by the peer review process. Consequently, having introduced peer review, the Elsevier journal Medical Hypotheses may be unable to continue its tradition as a radical journal allowing discussion of improbable or unconventional ideas. Hence we conclude by asking the publisher to consider re-introducing the system of editorial review to Medical Hypotheses. (shrink)

The "history of mentalities" considers the attitudes of ordinary people to everyday life. The approach is closely identified with the work of the Annales school. However, whereas the Annales historians refer to the material factors which condition human life, historians investigating mentalities examine psychological underpinnings. Historians who first developed guidelines for the history of mentalities were Lucien Febvre and Marc Bloch, who were both concerned with collective systems of belief. Later, Philippe Ariès and Norbert Elias identified and developed theories on (...) early childhood. Finally, Michel Foucault considered the psychology of social deviants and nonconformists. This mode of interpretation provides a means of examining those aspects of life which the linear approach cannot address, such as the pressure of conformity, the sense of accelerating time, and the preoccupation with self. (shrink)

This volume is a wonderful introduction to Foucault and a testimony to the deep humanity of the man himself.

Hutton considers the ideas of philosophers, poets, and historians to seek outthe roots of fact as mere recollection.

THE ART OF CAUSAL CONJECTURE Glenn Shafer Table of Contents Chapter 1. Introduction........................................................................................ ...........1 1.1. Probability Trees..........................................................................................3 1.2. Many Observers, Many Stances, Many Natures..........................................8 1.3. Causal Relations as Relations in Nature’s Tree...........................................9 1.4. Evidence............................................................................................ ...........13 1.5. Measuring the Average Effect of a Cause....................................................17 1.6. Causal Diagrams..........................................................................................20 1.7. Humean Events............................................................................................23 1.8. Three Levels of Causal Language................................................................27 1.9. An Outline of the Book................................................................................27 Chapter 2. Event Trees............................................................................................... .....31 2.1. Situations and Events...................................................................................32 2.2. The Ordering of Situations and Moivrean Events.......................................35 2.3. Cuts................................................................................................ ..............39 2.4. Humean Events............................................................................................43 (...) 2.5. Moivrean Variables......................................................................................49 2.6. Humean Variables........................................................................................53 2.7. Event Trees for Stochastic Processes...........................................................54 2.8. Timing in Event Trees.................................................................................56 2.9. Intersecting Event Trees...............................................................................60 2.10. Notes on the Literature...............................................................................61 Chapter 3. Probability Trees...........................................................................................63 3.1. Some Types of Probability Trees.................................................................64 ii 3.2. Axioms for the Probabilities of Moivrean Events.......................................68 3.3. Zero Probabilities....................................................................................... ..70 3.4. A Sample-Space Analysis of the Event-Tree Axioms.................................72 3.5. Probabilities and Expected Values for Variables.........................................74 3.6. Martingales......................................................................................... .........79 3.7. The Expectation of a Variable in a Cut........................................................83 3.8. Conditional Expected Value and Conditional Expectation.........................87 Chapter 4. The Meaning of Probability...........................................................................91 4.1. The Interpretation of Expected Value..........................................................92 4.2. The Interpretation of Expectation................................................................95 4.3. The Long Run..............................................................................................98 4.4. Changes in Belief.........................................................................................101 4.5. The Empirical Validation of Probability......................................................106 4.6. The Diversity of Uses of Probability...........................................................108 4.7. Notes on the Literature.................................................................................110 Chapter 5. Independent Events.......................................................................................113 5.1. Independence........................................................................................ .......114 5.2. Weak Independence.....................................................................................118 5.3. The Principle of the Common Cause...........................................................121 5.4. Conditional Independence............................................................................128 5.5. Notes on the Literature.................................................................................133 Chapter 6. Events Tracking Events.................................................................................135 6.1. Tracking............................................................................................ ...........137 6.2. Tracking and Conditional Independence.....................................................142 6.3. Stochastic Subsequence...............................................................................143 6.4. Singular Diagrams for Stochastic Subsequence...........................................147 6.5. Conjunctive and Interactive Forks...............................................................149 Chapter 7. Events as Signs of Events..............................................................................153 iii 7.1. Sign................................................................................................ ..............154 7.2. Weak Sign................................................................................................ ....159 7.3. The Ethics of Causal Talk............................................................................160 7.4. Screening Off...............................................................................................16 2 Chapter 8. Independent Variables...................................................................................167 8.1. Unconditional Independence........................................................................170 8.2. Conditional Independence............................................................................175 8.3. Independence for Partitions.........................................................................177 8.4. Independence for Families of Variables......................................................182 8.5. Individual Properties of the Independence Relations...................................186 Chapter 9. Variables Tracking Variables........................................................................189 9.1. Tracking and Conditional Independence: A Summary...............................190 9.2. Strong Tracking............................................................................................ 192 9.3. Strong Tracking and Conditional Independence..........................................198 9.4. Stochastic Subsequence...............................................................................201 9.5. Functional Dependence................................................................................203 9.6. Tracking in Mean.........................................................................................204 9.7. Linear Tracking............................................................................................ 207 9.8. Tracking by Partitions..................................................................................210 9.9. Tracking by Families of Variables...............................................................212 Chapter 10. Variables as Signs of Variables...................................................................215 10.1. Sign................................................................................................ ............219 10.2. Linear Sign................................................................................................ .222 10.3. Scored Sign................................................................................................ 225 10.4. Families of Variables.................................................................................227 Chapter 11. AnTheory of Event Trees.............................................................229 11.1. Event Trees as Sets of Sets........................................................................230 11.2. Event Trees as Partially Ordered Sets........................................................232 iv 11.3. Regular Event Trees...................................................................................240 11.4. The Resolution of Moivrean Variables......................................................244 11.5. Humean Events and Variables...................................................................246 Chapter 12. Martingale Trees..........................................................................................247 12.1. Examples of Decision Trees......................................................................249 12.2. The Meaning of Probability in a Decision Tree.........................................253 12.3. Martingales......................................................................................... .......257 12.4. The Structure of Martingale Trees.............................................................261 12.5. Probability and Causality...........................................................................265 12.6. Lower and Upper Probability.....................................................................269 12.7. The Law of Large Numbers.......................................................................272 12.8. Notes on the Literature...............................................................................274 Chapter 13. Refining............................................................................................ ...........275 13.1. Examples of Refinement............................................................................277 13.2. A Constructive Definition of Finite Refinement.......................................281 13.3. Axioms for Refinement..............................................................................282 13.4. Lifting Moivrean Events and Variables.....................................................288 13.5. Refining Martingale Trees.........................................................................288 13.6. Grounding........................................................................................... .......294 Chapter 14. Principles of Causal Conjecture..................................................................299 14.1. The Diversity of Causal Explanation.........................................................302 14.2. The Mean Effect of the Happening of a Moivrean Event..........................305 14.3. The Effect of a Humean Variable..............................................................311 14.4. Attribution and Generality.........................................................................316 14.5. The Statistical Measurement of the Effect of a Cause...............................319 14.6. Measurement by Experiment.....................................................................320 14.7. Using Our Knowledge of How Things Work............................................322 v 14.8. Sampling Error...........................................................................................329 14.9. The Sampling Frame..................................................................................329 14.10. Notes on the Literature.............................................................................330 Chapter 15. Causal Models.............................................................................................3 31 15.1. The Causal Interpretation of Statistical Prediction....................................333 15.2. Generalizing to a Family of Exogenous Variables....................................337 15.3 Some Joint Causal Diagrams......................................................................339 15.4. Causal Path Diagrams................................................................................342 15.5. Causal Relevance Diagrams.......................................................................346 15.6. The Meaning of Latent Variables..............................................................352 15.7. Notes on the Literature...............................................................................357 Chapter 16. Representing Probability Trees...................................................................359 16.1. Three Graphical Representations...............................................................361 16.2. Skeletal Simplifications.............................................................................368 16.3. Martingale Trees in Type Theory...............................................................371 Appendix A. Huygens’s Probability Trees.....................................................................379 Huygens’s Manuscript in Translation..................................................................380 Appendix B. Some Elements of Graph Theory..............................................................385 B1. Undirected Graphs........................................................................................385 B2. Directed Graphs............................................................................................38 6 Appendix C. Some Elements of Order Theory...............................................................393 C1. Partial and Quasi Orderings.........................................................................393 C2. Singular and Joint Diagrams for Binary Relations.......................................394 C3. Lattices............................................................................................ .............395 C4. The Lattice of Partitions of a Set..................................................................396 Appendix D. The Sample-Space Framework for Probability.........................................399 D1. Probability Measures....................................................................................399 D2. Variables........................................................................................... ...........400 vi D3. Families of Variables...................................................................................401 D4. Expected Value............................................................................................402 D5. The Law of Large Numbers.........................................................................405 D6. Conditional Probability................................................................................406 D7. Conditional Expected Value........................................................................407 Appendix E. Prediction in Probability Spaces................................................................409 E1. Conditional Distribution...............................................................................411 E2. Regression on a Single Variable...................................................................412 E3. Regression on a Partition or a Family of Variables......................................415 E4. Linear Regression on a Single Variable.......................................................418 E5. Linear Regression on a Family of Variables................................................422 Appendix F. Sample-Space Concepts of Independence.................................................425 F1. Overview............................................................................................ ...........426 F2. Independence Proper.....................................................................................432 F3. Unpredictability in Mean..............................................................................434 F4. Simple Uncorrelatedness..............................................................................437 F5. Mixed Uncorrelatedness...............................................................................438 F6. Partial Uncorrelatedness...............................................................................440 F7. Independence for Partitions..........................................................................442 F8. Independence for Families of Variables.......................................................445 F9. The Basic Role of Uncorrelatedness.............................................................448 F10. Dawid’s Axioms.........................................................................................449 Appendix G. Prediction Diagrams..................................................................................453 G1. Path Diagrams............................................................................................ ..454 G2. Generalized Path Diagrams..........................................................................462 G3. Relevance Diagrams.....................................................................................466 G4. Bubbled Relevance Diagrams......................................................................475 Appendix H. Abstract Stochastic Processes...................................................................479 vii H1. Probability Conditionals and Probability Distributions...............................477 H2. Abstract Stochastic Processes......................................................................479 H3. Embedding Variables and Processes in a Sample Space.............................480 References.......................................................................................... ..............................491. (shrink)

ABSTRACTEdith Stein’s study of empathy has much to offer to the current growth of research into empathy. This article first summarizes her phenomenological account of the complex layers involved in...

As a way of contributing to bioethics' understanding of itself, and, more particularly, to invigorate conversation about how we can best educate future colleagues, we present here a sketch of the quarter-century-old graduate concentration in medical ethics housed in the Department of Philosophy at the University of Tennessee, Knoxville. Our hope is to incite other programs to share their histories, strategies, problems, and aspirations, so as to help the field as a whole get a clearer sense of how we are (...) putting together our future, and of how we might best go about this important job. (shrink)

The American Journal of Bioethics, Volume 12, Issue 3, Page 46-48, March 2012.

Preparing the Next Generation of Oral Historians is an invaluable resource to educators seeking to bring history alive for students at all levels. Filled with insightful reflections on teaching oral history, it offers practical suggestions for educators seeking to create curricula, engage students, gather community support, and meet educational standards. By the close of the book, readers will be able to successfully incorporate oral history projects in their own classrooms.

Albert Einstein's attitude towards quantum mechanics—and statistical physics in general—was a puzzle to many of his contemporaries, and has remained a puzzle to the present. Though he made many significant contributions to statistical physics, he continually refused to regard that branch of science as fundamental. The present essay demonstrates that his attitude towards statistical physics was formed during his earliest investigations—between 1901 and 1903. In particular, it is shown that in Einstein's view, statistical laws are based upon non-statistical assumptions. This (...) view influenced much of his later thinking. A few illustrations are offered, concluding with a discussion of his opposition to quantum mechanics. (shrink)

On this occasion of honouring the achievement of Philip McShane, I would like to recall his earliest and, in my judgment, most important work, Randomness, Statistics and Emergence. In particular, I will recall how that work situated Lonergan’s important breakthrough on statistical method in relation to the major currents of thought on the subject, many of which remain influential still today.