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As testing programs transition from paper to online testing, they must study mode comparability to support the exchangeability of scores from different testing modes. To that end, a series of three mode comparability studies was conducted during the 2019–2020 academic year with examinees randomly assigned to take the ACT college admissions exam on paper or online. Unlike many prior mode comparability studies, examinees testing online earned higher scores on average than examinees testing on paper, especially on the English, reading, and writing tests. Much of the observed mode effects were accounted for by items near the ends of the tests. Subsequent examination of item omit rates, differential item functioning by mode, and change‐point analyses supported the notion that the observed mode effects were related to differential speededness. Specifically, examinees testing online were slightly less speeded, and were therefore able to perform better on items near the ends of the tests. Practical implications for linking across modes, test design, and test administration conditions are discussed. [ABSTRACT FROM AUTHOR]

The use of process-based modelling is increasing in most disciplines of plant science, but criteria for what is acceptable modelling work have rarely been made explicit. Unlike empirical work, modelling does not provide direct information on nature, and the goal is generally to explain natural phenomena, or predict their consequences, rather than reveal new phenomena or test specific hypotheses. Goals, methods and results of modelling and empirical work thus differ and they should be judged by different criteria. This note shows how the four criteria of explanation, testability, novelty and good modelling practice can be applied to modelling papers in the plant sciences. The practical implementation of these criteria is discussed, and a checklist of eight questions is derived with which papers can be evaluated. To illustrate the use of the criteria, they are applied to a classic publication from the crop modelling literature [Simulation of Assimilation, Respiration and Transpiration of Crops. Pudoc, Wageningen]. [Copyright &y& Elsevier]

Thematic analysis (TA) is one of the most popular methods in social science. There are several different approaches to TA that hold different ontological commitments, ranging from positivistic coding reliability TA to constructivist reflexive TA. However, there has been less focus on developing an approach that is informed by critical realism, with the notable exception of Wiltshire and Ronkainen. The first part of this paper proposes a five-step critical realist approach to TA. This approach aims to produce nuanced causal explanations of events, countering the mistaken assumption that qualitative research cannot produce causal knowledge. The second part of the paper brings this critical realist approach to TA into conversation with three alternatives: coding reliability, reflexive, and Wiltshire and Ronkainen's approach. The approach to TA in this paper builds on the strengths of these alternatives, offering an accessible way to adopt a critical realist philosophical grounding when doing TA. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers. [ABSTRACT FROM AUTHOR]

According to the research conducted so far, a crisp value is obtained by defuzzifying the fuzzy value using any defuzzification approach and this value is then used to analyze the entire problem. It is possible, however, that the crisp value generated using existing approaches may not exactly be matched with the fuzzy values. It is probable that any figure slightly early or slightly later than the obtained crisp value will produce the better outcome. So, the purpose of this research work is to provide a technique that can defuzzify fuzzy parameter values so that there are more than one possible values following a small adjustment to the technique. In this paper, the cost, demand and availability of transportation problems are taken in the form of trapezoidal fuzzy number. The advantage of the methodology developed is that the any value close to the crisp value obtained from the existing methods of trapezoidal fuzzy number or a value in the range of the trapezoidal fuzzy number as desired by the decision maker can be obtained. This method has the benefit of allowing the decision makers to receive as much crisp cost as desired in the fuzzy transportation problem. Which facilitated more accurate analysis of fuzzy transportation models. The numerical issue provides an explanation of the recommended strategy. [ABSTRACT FROM AUTHOR]

This article takes the issue of epistemology in writing for (performance) art to ask: 'What is the value of using "fictional" - as in "novelistic" - writing in reflective discourse on creative practice generally?' Using Susan Sontag's seminal essay 'Against Interpretation' as a starting point, the article argues that much writing on art assumes art's 'will-to-signify' - its value as a form of meaning - and consequently 'explanation' as the purpose of art writing. The problems with this reflex are discussed, including its suppression of alternative responses, which may include acknowledging that art is an affective entity: it has a function (if, in Kant's phrase, it is 'without purpose') and it has an ontology that may be more than its identity as signification. Extending, or restoring, the scope of art's reflective discourse in this way, the paper also notes, via reference to George Steiner, that a reciprocal extension for the media of this discourse is also possible, and it seeks to map the two extensions as the axes of a grid that offers varied combinations of the content-form dimensions of art writing. One of these conjunctions produces 'fictional writing' as a possible response to art. Seeming to dispel the problem of reductionism in explanatory discourse, the article then goes on to argue that the use of fiction in the spaces of art writing - 'Situational Fiction' - may be valuable in other ways as well. Hence, this is an argument for knowledge of creative practice in creative form. But 'Situational Fiction' may pursue this ethos of 'creative knowledge' in another way as well: as its reflexive dimension implicates the reader in deciding whether any aspect of this academic paper designates this work as 'fictional', as the paper understands this. [ABSTRACT FROM AUTHOR]

This paper presents arguments and evidence from psychology and neuroscience supporting Lipton's 2004 claim that scientists create knowledge through an abductive process that he calls "Inference to the Best Explanation". The paper develops two conclusions. Conclusion 1 is that without conscious effort on our part, our brains use a process very similar to abduction as a powerful way of interpreting sensory information. To support Conclusion 1, evidence from psychology and neuroscience is presented that suggests that what we humans perceive through our senses is not reality, but rather, our 'brain's "best guess" of the causes of its sensory input. The implication of this best guessing is that our brains use a process very similar to abduction throughout our lives to inform us of what is happening in the world around us. In addition, an argument based on Darwinian evolution is presented claiming that our brains do an excellent job of interpreting sensory information from the outside world. (If they did not, we, as a species, could hardly have survived.) Combining these two claims leads to Conclusion 1. Building on Conclusion 1, Conclusion 2 is that Lipton and others are correct in claiming that scientists use abduction when creating scientific theories. Abduction must be strong, because Nature chose abduction for its own sensemaking purposes. This paper's contribution to knowledge is in pointing out that recent psychological and neuroscientific research has major implications for the philosophical world's confidence in the probable validity of abductive inference. The punchline is simple: Nature chose abduction! [ABSTRACT FROM AUTHOR]

In this paper, we present the envisioned style and scope of the new topic "Explanation Paradigms Leveraging Analytic Intuition" (ExPLAIn) with the International Journal on Software Tools for Technology Transfer (STTT). Intention behind this new topic is to (1) explicitly address all aspects and issues that arise when trying to, if possible, reveal and then confirm hidden properties of black-box systems, or (2) to enforce vital properties by embedding them into appropriate system contexts. Machine-learned systems, such as Deep Neural Networks, are particularly challenging black-box systems, and there is a wealth of formal methods for analysis and verification waiting to be adapted and applied. The selection of papers of this first Special Section of ExPLAIn, most of which were co-authored by editorial board members, is an illustrative example of the style and scope envisioned: In addition to methodological papers on verification, explanation, and their scalability, case studies, tool papers, literature reviews, and position papers are also welcome. [ABSTRACT FROM AUTHOR]

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model's output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model's output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features' impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature's impact on the model's output. [ABSTRACT FROM AUTHOR]

Sober and Huemer have independently argued that simplicity has no place in evaluating philosophical views. In particular, they have argued that the best rationales for scientists to appeal to simplicity in judging between rival theories fail to carry over to philosophers judging between rival philosophical accounts. This paper disagrees with Sober and Huemer. It argues that two rationales for scientific appeals to simplicity equally well underwrite appeals to simplicity when philosophers evaluate rival rational reconstructions of some social normative practice. These two rationales are shown to apply to two philosophical appeals to simplicity: in Quine's argument against analyticity and in an argument against pluralism in accounts of scientific explanation. Some factors are identified that influence how much weight simplicity should carry in these and other philosophical cases. Simplicity's legitimate role in evaluating rival rational reconstructions suggests that simplicity will also turn out to be justly relevant to ontological investigations. [ABSTRACT FROM AUTHOR]

In developing a theory of consciousness, one of the main problems has to do with determining what distinguishes conscious states from non-conscious ones—the delimitation problem. This paper explores the possibility of solving this problem in terms of self-awareness. That self-awareness is essential to understanding the nature of our conscious experience is perhaps the most widely discussed hypothesis in the study of consciousness throughout the history of philosophy. Its plausibility hinges on how the notion of self-awareness is unpacked. The idea that consciousness involves self-awareness has been understood in two different ways: either as awareness of oneself—the subject of experience—or as awareness of the conscious episode itself. In this paper I argue (i) that every experience concerns the subject in a very specific way, involving what I will call 'perspectival de se representation', and (ii) that there is no need to appeal to the experience itself in order to characterize the awareness within that experience. The view I articulate explains the subjective nature of experience without over-intellectualizing it, accommodates the phenomenology of experience, and dispels any doubt about the need to find the self in introspection. [ABSTRACT FROM AUTHOR]

In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing. But existing methods are usually used to visualize important features or highlight active neurons, and few of them show the importance of relationships between features. In recent years, some methods based on a white-box approach have taken relationships between features into account, but most of them can only work on some specific models. Although methods based on a black-box approach can solve the above problems, most of them can only be applied to tabular data or text data instead of image data. To solve these problems, we propose a local interpretable model-agnostic explanation approach based on feature relationships. This approach combines the relationships between features into the interpretation process and then visualizes the interpretation results. Finally, this paper conducts a lot of experiments to evaluate the correctness of relationships between features and evaluates this XAI method in terms of accuracy, fidelity, and consistency. [ABSTRACT FROM AUTHOR]

This paper considers Minimal Yield, which is a condition on MERGE, and removes the redundancy between Minimal Search and the Phase Impenetrability Condition in warranting the condition when internal MERGE applies. By proposing that Transfer applies freely, the paper claims that Minimal Yield is guaranteed without reference to Minimal Search, providing empirical evidence for the proposed approach and demonstrating its superiority over Minimal Search. As a consequence of the proposal, A-movement is reconsidered. I claim that A-movement is not due to internal MERGE but to external MERGE, arguing for uniform association of phase-internal/cross-phasal relations with external/internal MERGE. This paper concludes that genuine explanation is possible for language. [ABSTRACT FROM AUTHOR]

It is argued in this paper that Shaikh has presented two different interpretations of the transformation problem in his works. In his influential 1977 paper, he presented an "iterative" interpretation in which there is a transfer of value between departments in the economy; and in a series of papers in the 1980s and in his recent book, he presented a different interpretation in which there is a transfer of value between firms in Department 3 and capitalist households. An important common feature of these two different interpretations is that total profit is not equal to total surplus-value. The two interpretations present different explanations of this divergence, which are examined in detail in this paper. It is argued that there is no textual evidence whatsoever to support Shaikh's second interpretation. A few comparisons will also be made to my "macro-monetary" interpretation of Marx theory according to which there is no transformation problem in Marx's theory and total profit is always equal to total surplus-value. [ABSTRACT FROM AUTHOR]

Knauff and Gazzo Castañeda (2022) object to using the term "new paradigm" to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term "paradigm" may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. [ABSTRACT FROM AUTHOR]

This paper explores the implications of conceptualising phenomenology as explanatory for the ongoing dialogue between the phenomenological tradition and cognitive science, especially enactive approaches to cognition. The first half of the paper offers three interlinked arguments: Firstly, that differentiating between phenomenology and the natural sciences by designating one as descriptive and the other as explanatory undermines opportunities for the kind of productive friction that is required for genuine 'mutual enlightenment'. Secondly, that conceiving of phenomenology as descriptive rather than explanatory risks committing us to what Zahavi (2019) identifies as the error of equating the phenomenological with the phenomenal. Finally, that the erroneous reduction to the descriptive occludes the rich resources that the phenomenological tradition can contribute to investigations of non-human cognition. The second half of the paper then turns to focus specifically on the promising relationship between phenomenology and enactive approaches to cognition. It will suggest that phenomenology must be seen as having explanatory capacities if it is to shed light on the structures of "mind in life" (Thompson, 2007), before drawing on the model of explanation put forward by Louis Sass to explore what this might look like. [ABSTRACT FROM AUTHOR]

In this paper, I consider the relationship between coming to understand why something must be the case and coming to understand why it actually is the case in some particular instance. Peter Lipton uses the possibility of coming to understand a phenomenon via a necessity proof as an argument that there can be understanding with no explanation. Lipton's argument has come under criticism, at least partially because one might think that understanding why something must be the case has a different object from understanding the actual phenomenon. In this paper, I argue that that under certain circumstances it is fairly straightforward to come to understand an actual phenomenon on the basis of knowing why it had to be the case. Rather than relying on brute intuitions, my primary strategy will be to show that on least some modern accounts of understanding we can validate Lipton's general point with respect to a variety of possible necessity relations. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model's capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings. [ABSTRACT FROM AUTHOR]

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This paper holds that Laozi's philosophy on softness is a topic that remains to be fully discussed. By distinguishing between the meanings of softness and weakness, this paper discusses how Wang Bi semantically integrated the two, presenting them as methods to attain the Dao. In this paper, the differences in Wang Bi's usage of "柔弱" (softness and weakness) and "柔顺" (softness and compliance) in his annotations on the Daodejing and Yijing are noted, emphasizing the logical support and rational explanation that Wang Bi provided for the external behaviors of gentleness described in the hexagram lines. Wang Bi reconciled the contradictions between Confucian and Daoist views on valuing gentleness and balancing Yin–Yang. In the text, he elaborates on gentleness as both a personal moral requirement and a method of social governance, addressing the real-world issues of his time and thus greatly enhancing the practicality of Laozi's philosophy of valuing softness. [ABSTRACT FROM AUTHOR]

This paper argues that the project of a lifespan perspective in developmental psychology has not yet been systematically pursued. Overall, the number of age-specific papers far outweighs the number of lifespan approaches, and even approaches that focus on the lifespan as a whole are often restricted to adulthood. Further, there is a lack of approaches that examine cross-lifespan relationships. However, the lifespan perspective has brought with it a "processual turn" that suggests an examination of developmental regulatory processes that are either operative across the lifespan or develop across the lifespan. Accommodative adjustment of goals and evaluations in response to obstacles, loss, and threat is discussed as an example of such a process. Not only is it prototypical of efficacy and change of developmental regulation across the lifespan, but at the same time it makes clear that stability (e.g., of the self)—as a possible outcome of accommodation—is not an alternative to, but a variant of development. Explaining how accommodative adaptation changes, in turn, requires a broader perspective. For this purpose, an evolutionary approach to developmental psychology is proposed that not only views human development as a product of phylogenesis, but also applies the central concepts of the theory of evolution (adaptation and history) directly to ontogeny. The challenges, conditions, and limitations of such a theoretical application of adaptation to human development are discussed. [ABSTRACT FROM AUTHOR]

Philosophical theorizing on democracy typically assumes away the existence of nonhuman animals – even though animals are systematically affected by democratic decisions in morally relevant ways. This paper inspects under which circumstances, if at all, this omission can be justified. First, I distinguish between two possible explanations of this neglect: (i) that animals are neglected by democratic theorists due to speciesist biases, according to a debunking account, or (ii) that animals are deliberately idealized away, on a reconstructive approach. Largely, this paper is devoted to assessing when assuming away animals can function as a theoretically productive or practically helpful idealization when theorizing about democracy. In particular, I identify three different aims that may be served by idealizing animals away in democratic theory: (i) to understand what makes democracy valuable, legitimate, or authoritative, (ii) to assess how the pitfalls of really existing democracies might be fixed and (iii) to isolate what robust agents owe each other in democratic societies. I then argue that, whereas the third goal might be usefully advanced by idealizing animals away, the first two goals can only be suitably tackled if we take into account how democracies affect animals’ interests. [ABSTRACT FROM AUTHOR]

In a seminal paper, Kiparsky & Kiparsky (1970) proposed a two‐way correspondence between presuppositionality of clauses and nominal structure. The proposal remains highly relevant to current research (a.o. Kastner 2015, Bochnak & Hanink 2022), despite the existence of counterexamples in both directions. In this paper, we examine Russian nominalized clauses to show that presuppositionality is indeed neither necessary nor sufficient for nominalization. However, instead of completely discarding the correspondence between presuppositionality and nominalization, we argue for a weaker "preference" hypothesis, whereby presuppositional, or discourse‐given, contexts are associated with a higher likelihood of nominalization compared to discourse‐new contexts. We provide support for the preference hypothesis based on four experimental studies, a forced‐choice and a givenness‐rating study using matrix negation as a proxy for givenness and a forced‐choice and a sentence completion study directly manipulating the discourse context. We suggest a tentative explanation for the preference hypothesis in terms of definiteness/familiarity marking. [ABSTRACT FROM AUTHOR]

We studied a paper to apply a method of maximizing deviations to multiple attribute decision-makings under intuitionistic fuzzy environment that have found several new methods and theorems for maximum problem under some specific conditions with insufficient information environment. We showed that the Lagrange multiplication method used by the paper can be replaced by our simplify approach with the Cauchy Schwarz inequality. The purpose of this paper is fourfold. First, the iteration method for the problem within the range of weights is well developed and with appropriate explanation if the weight vector of attributes is bounded. Second, if the weight vector of attributes is completely unknown, we could directly and swiftly derive the weight by the Cauchy-Schwarz inequality such that the complicated approach by the Lagrange multiplication method becomes redundant. Third, we prove the results of score function and rank for one-norm will not be preserved in the two-norm. Fourth, the same numerical examples are examined again and have different outcome to demonstrate our findings is superior to the previously published results. [ABSTRACT FROM AUTHOR]

This paper addresses the question of how the ups and downs in the development of artificial intelligence (AI) since its inception can be explained. It focuses on the development of artificial intelligence in Germany since the 1970s, and particularly on its current dynamics. An assumption is made that a mere reference to rapid advances in information technologies and the various methods and concepts of artificial intelligence in recent decades cannot adequately explain these dynamics, because from a social science perspective, this is an oversimplified, technology-centred explanation. Drawing on ideas from social scientific innovation research, the hypothesis is rather that artificial intelligence should be understood as a "promising technology". Its various stages of development have always been driven by technological promises about its special powers and capabilities when applied to solving economic and societal challenges. [ABSTRACT FROM AUTHOR]

Despite recent essentialist approaches to Kant’s laws of nature, it is unclear whether Kant’s critical philosophy is compatible with core tenets of essentialism. In this paper, I first reconstruct Kant’s position by identifying the key metaphysical and epistemological features of his notion of ‘nature’ or ‘essence’. Two theses about natures can be found in the literature, namely that they are noumenal in character (noumenal thesis) and that they guide scientific investigation as regulative ideas of reason (regulative thesis). I argue that Kant’s notion of nature does not entail the noumenal thesis and, based on his model of causal explanation, I propose a novel, phenomenal thesis, that allows for a better understanding of the function of natures as regulative ideas. In the last part of the paper, I show that Kant’s ‘essentialism’ is a genuine form of essentialism committed to de re modality, although it differs in several respects from major contemporary essentialist accounts. I conclude by suggesting that Kant’s essentialism (if appropriately updated) can be relevant to the contemporary debate, which has so far been dominated by Humean and Aristotelian proposals. [ABSTRACT FROM AUTHOR]

In a seminal paper, Kiparsky & Kiparsky (1970) proposed a two‐way correspondence between presuppositionality of clauses and nominal structure. The proposal remains highly relevant to current research (a.o. Kastner 2015, Bochnak & Hanink 2022), despite the existence of counterexamples in both directions. In this paper, we examine Russian nominalized clauses to show that presuppositionality is indeed neither necessary nor sufficient for nominalization. However, instead of completely discarding the correspondence between presuppositionality and nominalization, we argue for a weaker "preference" hypothesis, whereby presuppositional, or discourse‐given, contexts are associated with a higher likelihood of nominalization compared to discourse‐new contexts. We provide support for the preference hypothesis based on four experimental studies, a forced‐choice and a givenness‐rating study using matrix negation as a proxy for givenness and a forced‐choice and a sentence completion study directly manipulating the discourse context. We suggest a tentative explanation for the preference hypothesis in terms of definiteness/familiarity marking. [ABSTRACT FROM AUTHOR]

This paper responds to Friend's (2023) critique of the Powers-BSA , a view according to which laws of nature are efficient descriptions of how modally laden properties (powers) are possibly distributed in spacetime. In the course of this response, the paper discusses the nature of scientific and metaphysical explanation, the aim of science, and the structure of modal space. [ABSTRACT FROM AUTHOR]

When deciding where to visit next while traveling in a group, people have to make a trade-off in an interactive group recommender system between (a) disclosing their personal information to explain and support their arguments about what places to visit or to avoid (e.g., this place is too expensive for my budget) and (b) protecting their privacy by not disclosing too much. Arguably, this trade-off crucially depends on who the other group members are and how cooperative one aims to be in making the decision. This paper studies how an individual's personality, trust in group, and general privacy concern as well as their preference scenario and the task design serve as antecedents to their trade-off between disclosure benefit and privacy risk when disclosing their personal information (e.g., their current location, financial information, etc.) in a group recommendation explanation. We aim to design a model which helps us understand the relationship between risk and benefit and their moderating factors on final information disclosure in the group. To create realistic scenarios of group decision making where users can control the amount of information disclosed, we developed TouryBot. This chat-bot agent generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain [more specifically, the initial POI options were selected from the category of "Food" in Amsterdam (see Sect. 3.2 for the details)]. To understand the dynamics between the factors mentioned above and information disclosure, we conducted an online, between-subjects user experiment that involved 278 participants who were exposed to either a competitive task (i.e., instructed to convince the group to visit or skip a recommended place) or a cooperative task (i.e., instructed to reach a decision in the group). Results show that participants' personality and whether their preferences align with the majority affect their general privacy concern perception. This, in turn, affects their trust in the group, which affects their perception of privacy risk and disclosure benefit when disclosing personal information in the group, which ultimately influences the amount of personal information they disclose. A surprising finding was that the effect of privacy risk on information disclosure is different for different types of tasks: privacy risk significantly impacts information disclosure when the task of finding a suitable destination is framed competitively but not when it is framed cooperatively. These findings contribute to a better understanding of the moderating factors of information disclosure in group decision making and shed new light on the role of task design on information disclosure. We conclude with design recommendations for developing explanations in group decision-making systems. Further, we propose a theory of user modeling that shows what factors need to be considered when generating such group explanations automatically. [ABSTRACT FROM AUTHOR]

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance. [ABSTRACT FROM AUTHOR]

This paper introduces a special issue focusing on the intricacies of Turkey's party system and the evolution of Turkish party competition amid democratic challenges. The introduction provides a brief overview of each paper in this special issue, which delves into the relationship between economic factors and voter behavior, offering insights into the continued dominance of the ruling party. While doing so, the special issue specifically examines the 2023 Turkish General elections, presenting alternative perspectives on how the incumbent party maintains its electoral success. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) has recently gained visibility as one of the main topics of Artificial Intelligence research due to, among others, the need to provide a meaningful justification of the reasons behind the decision of black-box algorithms. Current approaches are based on model agnostic or ad-hoc solutions and, although there are frameworks that define workflows to generate meaningful explanations, a text classification framework that provides such explanations considering the different ingredients involved in the classification process (data, model, explanations, and users) is still missing. With the intention of covering this research gap, in this paper we present a text classification framework called OBOE (explanatiOns Based On concEpts), in which such ingredients play an active role to open the black-box. OBOE defines different components whose implementation can be customized and, thus, explanations are adapted to specific contexts. We also provide a tailored implementation to show the customization capability of OBOE. Additionally, we performed (a) a validation of the implemented framework to evaluate the performance using different corpora and (b) a user-based evaluation of the explanations provided by OBOE. The latter evaluation shows that the explanations generated in natural language express the reason for the classification results in a way that is comprehensible to non-technical users. [ABSTRACT FROM AUTHOR]

In a recent paper in this journal, Jonathan Barker argues against the claim that grounded entities are ontologically innocent. In this paper I defend the ontological innocence of grounded entities against Barker's argument. I tease out an assumption that is crucial for the success of Barker's argument and I show that the defender of ontological innocence can deny this assumption in a motivated way. [ABSTRACT FROM AUTHOR]

This paper proposes an account of why proof is the only way to acquire knowledge of some mathematical proposition's truth. Admittedly, non-deductive arguments for mathematical propositions can be strong and play important roles in mathematics. But this paper proposes a necessary condition for knowledge that can be satisfied by putative proofs (and proof sketches), as well as by non-deductive arguments in science, but not by non-deductive arguments from mathematical evidence. The necessary condition concerns whether we can justly expect that if the mathematical proposition is actually false, despite the evidence for its truth, then this fact has an explanation. [ABSTRACT FROM AUTHOR]

Purpose: Though there is emerging research that induces a postulation for a Vedic–Buddhist (V–B) cultural cluster, good theory development requires not only generalizability but also strong explanation. This paper aims to address the explanation gap to strengthen emerging theory development. Design/methodology/approach: Religion-derived spiritual philosophy travel is traced from historical origins in India to contemporary Japanese management practice and its underpinning values. Findings: The enhanced explanation developed in this paper finds a clear trace of spiritual values with roots in India surfacing in contemporary Japanese management as identified in extant cross-cultural management (CCM) literature. Research limitations/implications: This paper offers important explanation to strengthen emerging theory on the novel idea of a V–B CCM cluster. Practical implications: The strengthening of explanation for emerging theory adds to the case for modification of the traditional CCM meta-narrative that has positioned India and Japan in separate cultural clusters. Social implications: Strengthening the postulation of a V–B cultural cluster potentially lubricates foreign investment from Japan to India contributing to achievement of United Nations Sustainable Development Goal no. 17 that pertains to international partnerships. Additionally, the findings raise questions for public policymakers who in modern times occlude religion from the public sphere. Originality/value: This paper offers novel explanatory perspectives for emerging CCM theory, potentially expanding the spiritual philosophy avenue of management research. [ABSTRACT FROM AUTHOR]

The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics. [ABSTRACT FROM AUTHOR]