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Bounds consequence provides an interpretation of a multiple-conclusion consequence relation in which the derivability of a sequent "Image missing"is understood as the claim that it is conversationally out-of-bounds to take a position in which each member of Γ is asserted while each member of Δ is denied. Two of the foremost champions of bounds consequence—Greg Restall and David Ripley—have independently indicated that the shape of the bounds in question is determined by conversational practice. In this paper, I suggest that the standard treatments of bounds consequence have focused heavily on the matter of veridicality at the expense of ignoring other features by which conversational bounds are set, prime among them being the matter of content or subject-matter. Furthermore, I argue that the semantic behavior of propositions containing “monstrous” content—content whose introduction is inappropriate to a context independently of veridical considerations—leads to a weak Kleene account of bounds consequence.

In Suppose and Tell, Williamson makes a new case for the material conditional account. He tries to explain away apparently countervailing data by arguing that these have been misinterpreted because researchers have overlooked the role of heuristics in the processing of conditionals. Cases involving the receipt of apparently conflicting conditionals play an important dialectical role in Williamson’s book: they are supposed to provide evidence for the material conditional account as well as for the defeasibility of a key procedure underlying our everyday assessments of conditionals. We argue that they can serve neither of these purposes and that Williamson overestimates the reach of heuristics. We specifically challenge Williamson’s assumption that, in the kind of cases centrally at issue in his book, the recipient of conflicting conditionals will typically accept those at face value, even granting Williamson that conditionals can be freely passed among speakers under normal conditions of testimony.

Are psychiatric disorders natural kinds? This question has received a lot of attention within present-day philosophy of psychiatry, where many authors debate the ontology and nature of mental disorders. Similarly, historians of psychiatry, dating back to Foucault, have debated whether psychiatric researchers conceived of mental disorders as natural kinds or not. However, historians of psychiatry have paid little to no attention to the influence of (a) theories within logic, and (b) theories within metaphysics on psychiatric accounts of proper method, and on accounts of the nature and classification of mental disorders. Historically, however, logic and metaphysics have extensively shaped methods and interpretations of classifications in the natural sciences. This paper corrects this lacuna in the history of psychiatry, and demonstrates that theories within logic and metaphysics, articulated by Christian Wolff (1679–1754), have significantly shaped the conception of medical method and (psychiatric) nosology of the influential nosologist Boissier De Sauvages (1706–1767). After treating Sauvages, I discuss the method of the influential nosologist William Cullen (1710–1790), and demonstrate the continuity between the classificatory methods of Sauvages and Cullen. I show that both Sauvages and Cullen were essentialists concerning medical diseases in general and psychiatric disorders in particular, contributing to the history of conceptions of the ontology and nature of mental disorders.

We present epistemic multilateral logic, a general logical framework for reasoning involving epistemic modality. Standard bilateral systems use propositional formulae marked with signs for assertion and rejection. Epistemic multilateral logic extends standard bilateral systems with a sign for the speech act of weak assertion (Incurvati & Schlöder, 2019) and an operator for epistemic modality. We prove that epistemic multilateral logic is sound and complete with respect to the modal logic $\mathbf {S5}$ modulo an appropriate translation. The logical framework developed provides the basis for a novel, proof-theoretic approach to the study of epistemic modality. To demonstrate the fruitfulness of the approach, we show how the framework allows us to reconcile classical logic with the contradictoriness of so-called Yalcin sentences and to distinguish between various inference patterns on the basis of the epistemic properties they preserve.

This paper analyzes debates on animal language in eighteenth-century German philosophy and science. Adopting a history of ideas approach, I explain how the study of animal language became tied to the investigation into the origin and development of language towards the end of the eighteenth century. I argue that for large parts of the eighteenth century, the question of the existence of animal languages was studied within the context of the philosophical question of whether animals possess reason. In Germany, the debate concerning animal reason was influenced by Christian Wolff and was taken up by diverse thinkers such as Winkler, Meier, and Reimarus. I argue that in the second half of the eighteenth century the study of animal language became more loosely related to the question of whether animals possess reason: animal language was studied not only in light of the debate on animal reason but also because it sheds light on the nature of animals, on the differences and similarities between animals and humans, and on the origin and development of language. This systematic study of animal language coincided with the rise of linguistics, anthropology, and biology as independent sciences.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are “weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a—highly needed—standard for the communication among interdisciplinary areas of AI.

Public health, just as any policy-related field, faces the evergreen problem of turning knowledge into action. Among other problems, there is a clash between the inherent complexity of public health problems and the inevitable push, by decision-makers and the public, to simplify them. The Covid-19 pandemic has shown the insufficiencies of our current epistemological, methodological and normative apparatus to handle such crises in a timely manner. Despite this, several authors have been arguing for the importance of engaging global crises such as Covid-19 in ways that do not oversimplify key dimensions of the issues involved. In this paper, we contribute to this emerging scholarship. Building on existing work in the field of environmental problem-solving, we propose an integrative approach to navigating complex trade-offs in public health interventions. Briefly put, we propose that decision making should be informed by an analysis of any given problem from four distinct, but interrelated, lenses: (i) values and valuation, (ii) process and governance, (iii) power and inequalities and (iv) scientific evidence, methods and concepts. This normative framework, we argue, can help with spelling out the complexity of public health problems and with spelling out the rationale behind public health decision making to non-specialists and the general public. We illustrate our approach using the controversy over wearing face masks in the Covid-19 pandemic.

Music is an integral part of daily human life, and certain types of music are often associated with certain contexts, such as specific music for sleeping or for studying. The mood-arousal hypothesis suggests that music used for studying should be uplifting to boost arousal and increase cognitive performance while previous studies suggest that music used as a sleep aid should be calm, gentle and slow to decrease arousal. In this study, we created the Study music dataset by collecting tracks from Spotify playlists with the words ‘study’ or ‘studying’ in the title or description. In comparison with a pre-existing dataset, the Sleep music dataset, we show that the music’s audio features, as defined by Spotify, are highly similar. Additionally, they share most of the same genres and have similar subgroups after a k-means clustering analysis. We suggest that both sleep music and study music aim to create a pleasant but not too disturbing auditory environment, which enables one to focus on studying and to lower arousal for sleeping. Using large Spotify-based datasets, we were able to uncover similarities between music used in two different contexts one would expect to be different.

This paper analyzes the historical context and systematic importance of Kant's hypothetical use of reason. It does so by investigating the role of hypotheses in Kant's philosophy of science. We first situate Kant's account of hypotheses in the context of eighteenth-century German philosophy of science, focusing on the works of Wolff, Meier, and Crusius. We contrast different conceptions of hypotheses of these authors and elucidate the different theories of probability informing them. We then adopt a more systematic perspective to discuss Kant's idea that scientific hypotheses must articulate real possibilities. We argue that Kant's views on the intelligibility of scientific hypotheses constitute a valuable perspective on scientific understanding and the constraints it imposes on scientific rationality.

A.J. Cotnoir has argued that we should distinguish between two notions of proper parthood: outstripped part and non-identical part. Outstripped parthood is an asymmetric relation, but non-identical parthood is not. We argue, first, that the intuitions Cotnoir uses to motivate these notions do not always give the right verdict; and, second, that systematic reasons for distinguishing these two notions of parthood have further counter-intuitive consequences. This means the distinction between two notions of proper parthood currently lacks adequate motivation.

Although citizens agree on the importance of objective scientific information, yet they tend to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Instead, they rely on shallow information on the web or social media often published for commercial or political incentives rather than the correctness and informational value. This paper presents an overview of the CLEF 2022 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.

Satirical imitation is a popular format of late-night comedy shows and can provide political entertainment and education. However, little research has been conducted on how satirists mark their satirical intent to clue audiences in on their intended messaging. This study investigates the prosodic marking of satirical imitation and contrasts it with prosodic marking of irony. We conducted a detailed case study of the prosodic marking in Alec Baldwin’s satirical imitation of Donald Trump in his audiobook You Can’t Spell America Without Me contrasted with both Baldwin’s and Trump’s regular voices. The analyzed corpus contained six hours of audio material across the three sources. Through a combination of automatic and manual coding, we measured average pitch, pitch variation, and speech rate. Our analysis did not reveal marking of satirical imitation by pitch or pitch variation. The satirical imitation was only marked by a faster speech rate than both baseline voices. These findings contrast with previous studies that identified a lower pitch, less pitch variation, and a slower speech rate as markers of verbal irony. Our study provides first evidence that satirical imitation is prosodically marked differently from verbal irony, with a faster speech rate as one potential marker.

In this paper, we extend the techniques of non-deterministic semantics for propositional logics introduced by Arnon Avron and Iddo Lev to provide an account of modal operators. We examine the counterparts of the standard concepts from deterministic modal logics in this setting, giving parallel accounts of necessity, possibility, frame conditions, and so forth. As a concrete application, we examine how to augment the logic C_min with a necessity operator, and detail analogies to stronger normal systems. Finally, we examine an extension of classical logic with a non-deterministic modality and discuss some of its properties and applications.

Background and Context. Valid assessment of understanding of programming concepts in primary school children is essential to implement and improve programming education. Objective. We developed and validated the Computerized Adaptive Programming Concepts Test (CAPCT) with a novel application of Item Response Theory. The CAPCT is a web-based and resource-efficient adaptive assessment of 4489 questions measuring: the understanding of basic sequences, loops, conditions (if & if-else statements), debugging, multiple agents, procedures, and the ability to generalize to a new syntax. Method. Data was collected through an existing online adaptive practice and monitoring system called Math Garden. We collected 14 million responses from 93,341 Dutch children (ages 4 - 13). Findings. The CAPCT demonstrated good psychometric qualities because 75% of the variance in question difficulty was explained by differences in item characteristics. The CAPCT demonstrated robustness against adding new participants and adding new items. Differences in player ability (i.e., understanding of CS concepts) were due to differences in age, gender, the number of items played, and prior mathematical ability. Implications. The CAPCT may be used by teachers to identify the level of programming concept understanding of their pupils, while researchers may use the CAPCT to construct and validate effective teaching resources.

The Web and social media have become the main source of information for citizens, with the risk that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. Non-experts tend to avoid scientific literature due to its complex language or their lack of prior background knowledge. Text simplification promises to remove some of these barriers. The CLEF 2022 SimpleText track addresses the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of NLP and IR researchers working together to resolve one of the greatest challenges of today. The track will use a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.

In this chapter, public health (PH) is considered as a diverse field in which a number of disciplines and approaches contribute to establishing a knowledge base for the design and implementation of interventions. I aim to explain why causality is central, yet not always explicit, in both the generation of the knowledge base and in the design process of interventions. I point to the importance of this diversity in PH and thus motivate for a pluralistic approach to causality, which has the potential to strengthen our knowledge base for PH interventions and the communication from PH scholars to decision makers and the general public.

Transfer learning is one of the prevailing approaches towards training language-specific BERT models. However, some languages have uncommon features that may prove to be challenging to more domain-general models but not domain-specific models. Comparing the performance of BERTić, a Bosnian-Croatian-Montenegrin-Serbian model, and Multilingual BERT on a Named-Entity Recognition (NER) task and Masked Language Modelling (MLM) task based around a rare phenomenon of indeclinable female foreign names in Serbian reveals how the different training approaches impacts their performance. Multilingual BERT is shown to perform better than BERTić in the NER task, but BERTić greatly exceeds in the MLM task. Thus, there are applications both for domain-general training and domain-specific training depending on the tasks at hand.

I argue that rejection cannot be reduced to assertion. Adapting an observation by Huw Price, I argue that rejection is best conceived of as the speech act that is used to register that some other speech act is (or would be) violating a rule of the conversation game. This can be understood as registering norm violations where speech acts are characterized by their essential norms. However, rejection itself cannot be characterized by a norm. Instead, registering violations is a necessary condition for grasping the conversation game. The core observation is that the concept of an ‘illegal move’ is intelligible, so a speech act can be (say) an assertion, despite violating the essential norm of asserting. Rejection has the function of pointing out that a move is illegal. Registering rule violations is a precondition of playing games with rules (it is part of the concept ‘game’), not itself a rule in a game. A similar special role of rejection (that it is not explicable in the terms provided by a conceptual framework, but needed to grasp these terms) likely occurs in other frameworks as well, e.g. when one characterizes speech acts by commitments or their effect on a common ground.

In previous publications, we have proposed a new, inferentialist semantics for indicative conditionals. According to this semantics, the truth of a conditional requires the existence of a compelling argument from the conditional’s antecedent together with contextually determined background premises to its consequent, where the antecedent is pivotal in the argument. In this paper, we recapitulate the position; report the progress we made over the past years, in particular highlighting the empirical support the position has garnered; and respond to criticisms that have been leveled at it.

In this chapter, we present and discuss an ongoing project to develop a glass-box AI engine called KRINO – from Greek, to judge, criticize, reason – capable of parsing written text on the discourse level and analyzing the arguments thereby contained. KRINO is designed to assist users with argument-checking, i.e., the process of evaluating the quality of arguments. We describe the set-up and basic characteristics of KRINO and explain how it can assist human annotators in a project undertaken by the Dutch organization Internet Society Netherlands Make Media Great Again Working Group (shortened MMGA) that is aimed at improving the quality of online information in settings varying from online news outlets to social media. The joint project is motivated by the challenges posed by online information and the need to empower internet users to better analyze its contents. We explain the prospects and challenges of combining the KRINO and MMGA projects on argument-checking and discuss the societal and computational relevance of this project.

Artificial Intelligence (AI) is quickly being taken-up across scientific disciplines, medical imaging is no exception. To stimulate development and facilitate the scientific evaluation of new approaches, AI-based research in medical imaging is increasingly organised in a competitive manner through digital machine-learning development platforms such as Kaggle and Grand Challenge—two of the leading platforms in the field. For medical image analysis, such competitions constitute an important research infrastructure, steering global research and development in this dedicated AI subfield. Yet, little is known about how these platform-based infrastructures that operate across the medical AI research pipeline shape the conditions for model production and evaluation. This paper addresses this issue through a critical empirical case study of 120 medical imaging competitions on Kaggle and Grand Challenge between 2017 and 2022. We show that platforms as well as competition organisers shape power relations in medical AI research at the level of data and task design, model production and evaluation in several distinct ways. Taken together, because competitions play a central rol within the field, these findings highlight the impact these powerful actors ultimately have on steering medical image AI research directions as they see fit and influence the types of models that are implemented into clinical settings.

This thesis aims to answer two questions about causal claims (such as sentences containing cause or because). Firstly, the modelling question: what kind of information do we use when we judge that a causal claim holds? Secondly, the meaning question: under what conditions do we judge that a causal claim is true? Our answer to the modelling question is that a causal model must contain time, part–whole structure, and nomic possibility. The model represents scenarios as extended in time, with each moment in time having a mereological structure (the mereological structure tell us, for example, that the state of Amsterdam is part the state of the Netherlands). The notion of nomic possibility specifies which worlds are nomically possible and which worlds are nomically impossible; in other words, which worlds satisfy the laws and which do not. In addition, the model must also contain two language-related components. For each sentence, the model must tell us what parts of the world the sentence is about, and in which worlds it is true. We show that this modelling framework is strictly more general than a popular alternative, that of structural causal models. Every structural causal model can be translated into our framework, and therefore every scenario that structural causal models can represent our framework can represent too. However, the converse does not hold. There are some scenarios that our proposed model can represent which structural causal models cannot. Our answer to the meaning question appeals to two relations: sufficiency and production. We propose that the meaning of cause and because is a blend of these relations: C cause E and E because C are true just in case C is true, and C is sufficient to produce E but C’s negation is not.

A core methodological approach in political science consists in comparing cases. Qualitative comparative analysis (QCA) is a methodology designed to do just that, in a systematic manner and exploiting strengths of both qualitative and quantitative approaches. This chapter presents QCA as a candidate methodology to go beyond the “qualitative-quantitative” divide and discusses whether, and to what extent, QCA helps address both why- and how-questions. Finally, the chapter discusses what it means to combine methods in an approach such as QCA, also implementing a mixed methods research (MMR) protocol. Using Ruphy’s approach of “foliated pluralism,” we explain that, from an epistemological perspective, the added value is that a pluralistic methodological approach such as QCA allows better and richer epistemic access to the phenomenon of interest, and also enables one to produce causal inferences.

We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty and represent the agent’s beliefs about probability, we consider a plausibility map, associating to each possible distribution a plausibility ranking. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds (or more generally, truth in all the worlds that are plausible enough). We consider two forms of conditioning or belief update, corresponding to the acquisition of two types of information: (1) learning observable evidence obtained by repeated sampling from the unknown distribution; and (2) learning higher-order information about the distribution. The first changes only the plausibility map (via a ‘plausibilistic’ version of Bayes’ Rule), but leaves the given set of possible distributions essentially unchanged; the second rules out some distributions, thus shrinking the set of possibilities, without changing their plausibility ordering.. We look at stability of beliefs under either of these types of learning, defining two related notions (safe belief and statistical knowledge), as well as a measure of the verisimilitude of a given plausibility model. We prove a number of convergence results, showing how our agent’s beliefs track the true probability after repeated sampling, and how she eventually gains in a sense (statistical) knowledge of that true probability. Finally, we sketch the contours of a dynamic doxastic logic for statistical learning.

In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” (2018) as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic advantages, and we propose a novel way to identify the latent camera through which the BBC archive was seen by machine.

In this paper we design a new logical system to explicitly model the different deductive reasoning steps of a boundedly rational agent. We present an adequate system in line with experimental findings about an agent’s reasoning limitations and the cognitive effort that is involved. Inspired by Dynamic Epistemic Logic, we work with dynamic operators denoting explicit applications of inference rules in our logical language. Our models are supplemented by (a) impossible worlds (not closed under logical consequence), suitably structured according to the effect of inference rules, and (b) quantitative components capturing the agent’s cognitive capacity and the cognitive costs of rules with respect to certain resources (e.g. memory, time). These ingredients allow us to avoid problematic logical closure principles, while at the same time deductive reasoning is reflected in our dynamic truth clauses. We further show that our models can be reduced to awareness-like plausibility structures that validate the same formulas and we give a sound and complete axiomatization with respect to them. This approach to the agent’s internal deductive reasoning is finally combined with actions of external information.

Purpose On November 19, 2020, a man was brutally murdered by security guards of a supermarket chain located in a city in the very south of Brazil, home to the world’s largest Afro-Black community outside Africa. This killing triggered protests across the country, as a Black man was killed one day before Black Awareness Day. This large popular mobilization happened at the end of 2020 and resembled the George Floyd protests. Despite the anger resonating across communities in Brazil and abroad, the news media coverage on social media resulted in an inequitable distribution of editorial space to both the affective and critical dimensions. As users often adopt view-based practices by not clicking on social media content shared through URLs, it is essential to understand how news media portrays content in short text posted online. Study design Under the lens of news frame and social media theories, our study was built on a multi-method approach combining computational, quantitative, qualitative methods to understand how news media portrays content in a tweet. To do so, we collected 267,576 tweets. They were filtered through the case’s perspective and analyzed using frame theory. Findings Results show that Brazilian news media value different angles concerning Mr. Freitas’s death and subsequent events. While traditional media tends to be more neutral in portraying protests and the killing, digital media raised the debate on the issue and about racism in the country, highlighting protests and using supporting hashtags. Furthermore, cultural hybridity can be seen in Brazil with Black Lives Matter (BLM) movement. Practical and social implications In a media system that shares some aspects with those found in the United States, such as highly commercial media, our study demonstrates the importance of digital media for articulating racial issues and segregation in Brazil. Originality/value A long tradition of scholarship in sociology and communication studies has investigated how news media frames social movements and the impact this coverage might have on protesters and their ability to gather public support in Western-rich democracies. Less well documented is the use of news frames in Brazil. Furthermore, it shows how BLM is hybridized with local topics and has gained traction with digital media outlets. This study aims to fill this gap by becoming the first study investigating how news media frames social movements against racism in Brazil.

Of the six possible orderings of the three main constituents of language (subject, verb, and object), two-SOV and SVO-are predominant cross-linguistically. Previous research using the silent gesture paradigm in which hearing participants produce or respond to gestures without speech has shown that different factors such as reversibility, salience, and animacy can affect the preferences for different orders. Here, we test whether participants' preferences for orders that are conditioned on the semantics of the event change depending on (i) the iconicity of individual gestural elements and (ii) the prior knowledge of a conventional lexicon. Our findings demonstrate the same preference for semantically conditioned word order found in previous studies, specifically that SOV and SVO are preferred differentially for different types of events. We do not find that iconicity of individual gestures affects participants' ordering preferences; however, we do find that learning a lexicon leads to a stronger preference for SVO-like orders overall. Finally, we compare our findings from English speakers, using an SVO-dominant language, with data from speakers of an SOV-dominant language, Turkish. We find that, while learning a lexicon leads to an increase in SVO preference for both sets of participants, this effect is mediated by language background and event type, suggesting that an interplay of factors together determines preferences for different ordering patterns. Taken together, our results support a view of word order as a gradient phenomenon responding to multiple biases.

In this article, we revisit a number of disputes regarding significance logics—that is, inferential frameworks capable of handling meaningless, although grammatical, sentences—that took place in a series of articles most of which appeared in the Australasian Journal of Philosophy between 1966 and 1978. These debates concern (i) the way in which logical consequence ought to be approached in the context of a significance logic, and (ii) the way in which the logical vocabulary has to be modified (either by restricting some notions, or by adding some vocabulary) to keep as much of classical logic as possible. Our aim is to show that the divisions arising from these disputes can be dissolved in the context of a novel and intuitive proposal that we put forward.

One of the more persistent debates in mereology is whether distinct wholes can have the same parts. Extensional mereologists hold that if there is no part that makes the difference, then there is nothing to distinguish the wholes, so sameness of parts implies identity. Non-extensionalists, however, do think there are cases where distinct wholes share all their parts. This paper argues that the kind of argument non-extensionalists employ can also be levelled against a widely accepted extensionality principle of plural logic. Non-extensionalists thus face a dilemma: either give up the argument for non-extensional mereology, or deny both the extensionality principle of mereology and the analogous principle for plural logic.

Meyer and Mortensen’s Alien Intruder Theorem includes the extraor- dinary observation that the rationals can be extended to a model of the relevant arithmetic R♯, thereby serving as integers themselves. Al- though the mysteriousness of this observation is acknowledged, little is done to explain why such rationals-as-integers exist or how they operate. In this paper, we show that Meyer and Mortensen’s models can be identified with a class of ultraproducts of finite models of R♯, providing insights into some of the more mysterious phenomena of the rational models.

Word order is a key property by which languages indicate the relationship between a predicate and its arguments. However, sign languages use a number of other modality-specific tools in addition to word order such as spatial agreement, which has been likened to verbal agreement in spoken languages, and role shift, where the signer takes on characteristics of propositional agents. In particular, data from emerging sign languages suggest that, though some use of a conventional word order can appear within a few generations, systematic spatial modulation as a grammatical feature takes time to develop. We experimentally examine the emergence of systematic argument marking beyond word order, investigating how artificial gestural systems evolve over generations of participants in the lab. We find that participants converge on different strategies to disambiguate clause arguments, which become more consistent through the use and transmission of gestures; in some cases, this leads to conventionalized iconic spatial contrasts, comparable to those found in natural sign languages. We discuss how our results connect with theoretical issues surrounding the analysis of spatial agreement and role shift in established and newly emerging sign languages, and the possible mechanisms behind its evolution.

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 (alleged) 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 critically this claim and argue that some current understandings of the practices that surround social science experimentation overestimate the degree to which experiments can actually fulfil this role as “objective” adjudicators, by neglecting the importance of shared background knowledge or assumptions and of consensus regarding the validity of the constructs involved in an experiment. We take issue with the way the distinction between internal and external validity is often used to comment on the inferential import of experiments, used both among practitioners and among philosophers of science. We describe the ways in which the more common (dichotomous) use of the internal/external distinction differs from Cook and Campbell’s original methodological project, in which construct validity and the four-fold validity typology were all important in assessing the inferential import of experiments. We argue that the current uses of the labels internal and external, as applied to experimental validity, help to encroach a simplistic view on the inferential import of experiments that, in turn, misrepresents their capacity to provide objective knowledge about the causal relations between variables.

This article explains Kant’s claim that sciences must take, at least as their ideal, the form of a ‘system’. I argue that Kant’s notion of systematicity can be understood against the background of de Jong and Betti’s Classical Model of Science (2010) and the writings of Georg Friedrich Meier and Johann Heinrich Lambert. According to my interpretation, Meier, Lambert and Kant accepted an axiomatic idea of science, articulated by the Classical Model, which elucidates their conceptions of systematicity. I show that Kant’s critique of the mathematical method is compatible with his adherence to this axiomatic conception of science. I further show that systematicity advances traditionally accepted logical ideals of scientific knowledge, which explains why Meier and Kant think that sciences must be ‘systematic’.

We explore the prospects of a monist account of explanation for both non-causal explanations in science and pure mathematics. Our starting point is the counterfactual theory of explanation (CTE) for explanations in science, as advocated in the recent literature on explanation. We argue that, despite the obvious differences between mathematical and scientific explanation, the CTE can be extended to cover both non-causal explanations in science and mathematical explanations. In particular, a successful application of the CTE to mathematical explanations requires us to rely on counterpossibles. We conclude that the CTE is a promising candidate for a monist account of explanation in both science and mathematics.

Donkey anaphora in quantified sentences is ambiguous between an existential and a universal reading. The extent to which different readings are accessible depends on the quantifier, but how to model this dependence is debated (Kanazawa, 1994; Champollion et al. 2019). This study advances this debate by providing novel experimental data on the interpretation of donkey anaphora in sentences with non-monotonic quantifiers exactly 3 and all but one. We establish that while the existential reading of donkey anaphora is the preferred one with both exactly 3 and all but one, the universal reading is accessed more with all but one than with exactly 3. These results have important implications for both Kanazawa (1994) and Champollion et al. (2019) theories, as both need to be amended to fully capture the empirical picture.

There is a noticeable gap between results of cognitive neuroscientific research into basic mathematical abilities and philosophical and empirical investigations of mathematics as a distinct intellectual activity. The paper explores the relevance of a Wittgensteinian framework for dealing with this discrepancy.

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 the improvement of empirically-riven work in the debates about mechanistic explanation of ecological phenomena.

This paper provides an introduction to the burgeoning field of the “history of the humanities”. It discusses the scope, goals, and challenges of this new discipline. While histories of separate humanities disciplines have been written since the early twentieth century, it is only over the last decade or so that we have witnessed works that ask the question: how do these separate histories fit together to form the history of the humanities? After an introduction to the origins and the development of the new discipline, we question why the history of the humanities emerged so late, especially compared to the history of science. We make a case for a comprehensive history of the humanities, and we discuss several problems and challenges for the field, i.e., the problems of eurocentrism and triumphalism, and the challenges of the global, comparative, polycentric, and multidisciplinary histories of the humanities. The paper concludes with a discussion about the future of the field, arguing that it should be opened up to the history of the non-academic humanities as well as the colonial humanities.

The discussion of collective agency involves the reduction problem of the concept of a collective. Individualism and Cartesian internalism have long restricted orthodox theories and made them face the tension between an irreducible concept of a collective and ontological reductionism. Heterodox theories as functionalism and interpretationism reinterpret the concept of agency and accept it as realized on the level of a collective. In order to adequately explain social phenomena that have relations as their essence, in this paper we propose a relational, holistic account of collective agency and argue that functionalism and interpretationism can be integrated into such an account.

Modified numerals, such as at least three and more than five, are known to sometimes give rise to ignorance inferences. However, there is disagreement in the literature regarding the nature of these inferences, their context dependence, and differences between at least and more than. We present a series of experiments which sheds new light on these issues. Our results show that (a) the ignorance inferences of at least are more robust than those of more than, (b) the presence and strength of the ignorance inferences triggered by both at least and more than depends on the question under discussion (QUD), and (c) whether ignorance inferences are detected in a given experimental setting depends partly on the task that participants are asked to perform (e.g., an acceptability task versus an inference task). We offer an Optimality Theoretic account of these findings. In particular, the task effect is captured by assuming that in performing an acceptability task, participants take the speaker’s perspective in order to determine whether an expression is optimal given a certain epistemic state, while in performing an inference task they take the addressee’s perspective in order to determine what the most likely epistemic state of the speaker is given a certain expression. To execute the latter task in a fully rational manner, participants have to perform higher-order reasoning about alternative expressions the speaker could have used. Under the assumption that participants do not always perform such higher-order reasoning but also often resort to so-called unidirectional optimization, the task effect finds a natural explanation. This also allows us to relate our finding to asymmetries between comprehension and production that have been found in language acquisition.

This article shows how a machine can employ a network view to reason about complex social relations of news reliability. Such a network view promises a topic-agnostic perspective that can be a useful hint on reliability trends and their heterogeneous assumptions. In our analysis, we depart from the ever-growing numbers of papers trying to find machine learning algorithms to predict the reliability of news and focus instead on using machine reasoning to understand the structure of news networks by comparing it with our human judgements. Understanding and representing news networks is not easy, not only because they can be extremely vast but also because they are shaped by several overlapping network dynamics. We present a machine learning approach to analyse what constitutes reliable news from the view of a network. Our aim is to machine-read a network’s understanding of news reliability. To analyse real-life news sites, we used the Décodex dataset to train machine learning models from the structure of the underlying network. We then employ the models to draw conclusions how the Décodex evaluators came to assess the reliability of news.

In this paper, we explain why the antecedent of a biscuit conditional is relevant to its consequent by extending Douvenʼs evidential support theory of conditionals making use of utilities. By this extension, we can also explain why a biscuit conditional gives rise to the inference that the consequence is (most likely) true. Finally, we account for the intuition that (indicative) biscuit sentences are false when the antecedent is false and allow for counterfactual biscuits.

Understanding the relationship between human cognition and linguistic structure is a central theme in language evolution research. Numerous studies have investigated this question using the silent gesture paradigm in which participants describe events using only gesture and no speech. Research using this paradigm has found that Agent–Patient–Action (APV) is the most commonly produced gesture order, regardless of the producer’s native language. However, studies have uncovered a range of factors that influence ordering preferences. One such factor is salience, which has been suggested as a key determiner of word order. Specifically, humans, who are typically agents, are more salient than inanimate objects, so tend to be mentioned first. In this study, we investigated the role of salience in more detail and asked whether manipulating the salience of a human agent would modulate the tendency to express humans before objects. We found, first, that APV was less common than expected based on previous literature. Secondly, salience influenced the relative ordering of the patient and action, but not the agent and patient. For events involving a non-salient agent, participants typically expressed the patient before the action and vice versa for salient agents. Thirdly, participants typically omitted non-salient agents from their descriptions. We present details of a novel computational solution that infers the orders participants would have produced had they expressed all three constituents on every trial. Our analysis showed that events involving salient agents tended to elicit AVP; those involving a non-salient agent were typically described with APV, modulated by a strong tendency to omit the agent. We argue that these findings provide evidence that the effect of salience is realized through its effect on the perspective from which a producer frames an event.

Articulations of discontinuity and moments of dissent have been central to critical historical work. However, such vocabularies and analyses of historical change have received less attention in the emerging field of digital methods. Digital methods based on discerning patterns have focused on continuities, while discontinuities and ruptures have been derivative of trends and patterns. By contrast, genealogical methods attend to the entanglement of continuity and discontinuity, and focus on contingency and singularity. This article proposes to develop methods of computational genealogy to analyze multiple temporalities in historical discourses. We experiment with our proposed computational genealogy using the archive of Inaugural speeches by US presidents. In particular, we show that there is neither a linear advance to Trump’s rhetoric nor an exceptional rupture. Our analysis shows that Trump’s speech is much more the struggle of the Republicans with their own past ideas than struggles with Democrats.

Efficiently exploiting all sources of information such as labeled instances, classes’ representation, and relations of them has a high impact on the performance of Multi-Label Text Classification (MLTC) systems. Most of the current approaches use labeled documents as the primary source of information for MLTC. We investigate the effectiveness of different sources of information— such as the labeled training data, textual labels of classes, and taxonomy relations of classes— for MLTC. More specifically, first, for each document–class pair, different features are extracted using different sources of information. The features reflect the similarity of classes and documents. Then, MLTC is considered to be a ranking problem, and a learning to rank (LTR) approach is used for ranking classes regarding documents and selecting labels of documents. An important characteristic of many MLTC instances is that documents can belong to multiple classes and there are implicit relations between classes. We apply score propagation on top of LTR to incorporate co-occurrence patterns of classes in labeled documents. Our main findings are the following. First, using an LTR approach integrating all features, we observe significantly better performance than previous systems for MLTC. Specifically, we show that simple classification approaches fail when there is a high number of classes. Second, the analysis of feature weights reveals the relative importance of various sources of evidence, also giving insight into the underlying classification problem. Interestingly, the results indicate that the titles of documents are more informative than all other sources of information. Third, a lean-and-mean system using only four features is able to perform at 96% of the large LTR model that we propose in this paper. Fourth, using the co-occurrence information of classes helps in classifying documents more accurately. Our results show that the co-occurrence information is more helpful when the underlying classifier has a poor performance.

While function modeling has been around in engineering design research since the 1960s, there have been no systematic, comparative studies devoted to assessing the adequacy of function modeling frameworks in light of engineering design objectives. This systematic assessment and comparison – called benchmarking – is now recognized as a central research issue in current function modeling research, but insight into how this benchmarking can be done is at present limited. In this paper, we attempt to improve our insight into how benchmarking can be done for a specific but important engineering context: function optimization of reverse-engineered systems. We argue that the capacity to produce technical advantage predictions, viz. predictions concerning the improved functional performance of a redesigned technical system, is an important benchmark criterion in this context. We subsequently illustrate the utility of the criterion by assessing two prominent function modeling frameworks in terms of it. Throughout the paper, we use a case study of the design of an electric wok to clarify and illustrate our ideas.