Alba Curry

This chapter reconstructs a Mohist conception of esteem as a form of political technology. Focusing on the paired doctrines of “Promoting the worthy” and “Identifying upward,” I argue that the Mozi develops an “economy of esteem” in which recognition is publicly structured to align status, authority, and material reward with contributions to the common good. Esteem, on this view, is not primarily an affective attitude but an institutional mechanism for coordinating behaviour and securing social order. I then turn to the “Denying the Ru” chapter, reading it as a performative intervention within this economy of esteem. On this reading, its polemical force helps to mark and enforce the boundaries of worth by actively discrediting rival practices of recognition. Finally, I assess two pressures on the Mohist view, concerning its apparent affective thinness and its susceptibility to strategic performance. I argue that these features can be accommodated within the Mohist framework and that, rather than undermining the view, they help to illuminate its distinctive commitment to treating esteem primarily as a tool for shaping collective life rather than a response to inner moral worth.

This paper examines the possibilities and challenges of defining a Brazilian philosophy within a decolonial and feminist framework. While Brazilian philosophy occupies a marginal position in university curricula across Brazil, a wealth of philosophical work is produced in the country. Nonetheless, the classification of this body of thought as distinctly ‘Brazilian’ remains a matter of critical and ongoing debate. We argue that this debate reflects broader struggles over national identity, coloniality, and epistemic self-determination and we propose a vision for what Brazilian philosophy could and should be, drawing inspiration from dialogues with other non-Western philosophical traditions such as Latin American, African and Chinese philosophies as well as the efforts of feminists movements to broaden the philosophical canon. Our proposal of Brazilian philosophy engages with Brazil’s national identity by emphasizing Lélia Gonzalez’s category of amefricanity (2020), which refers to the Indigenous and African heritage in Brazil. In doing so, we present both Brazilian Indigenous systems of thought and the African-diasporic philosophies produced in the country as constituent parts of Brazilian philosophy.

Taylor Swift is now part of a long tradition of mad women. Swift, perhaps best known for her takes on love, embodies all-American femininity: she is beautiful, poised and articulate, speaks with a soft voice, sings about kisses in cars and football games, she is described as the philosopher of forgiveness by the New York Times. And yet her tears are bullets, they ricochet; there is nothing she does better than revenge. This spite deviates from the good deportment we expect from a woman. To her predominantly female audience, her scathing lyrics are a sort of feminist manifesto (there are countless playlists of Swift's angry songs, including no body, no crime where she leans into the trope of vengeful, murderous women) but for non-swifties her anger is nothing short of a tantrum from a spoiled brat. In this chapter we discuss what anger is, how it’s gendered, and its ethical value for women through the lens of Taylor Swift’s oeuvre and headlines, or in Swift's own words, we make the value of women’s anger fade into view.

This chapter discusses the Joker through the lens of ancient Chinese philosophy. We argue that Batman represents a kind of Confucian ethics in which we must play our social roles and live up to our values. The Joker, on the other hand, is better understood as a Daoist—turn especially to the early Daoist Zhuangzi and a famous “wild card” interpretation of him. As a Daoist wild card, the Joker personifies an elemental chaos—different from evil—which calls into question our values and commitments precisely by having no values and commitments of his own.

This chapter examines Aristotle's account of fear in the Rhetoric and its significance in the Politics. For Aristotle, fear is not a blind reflex, but a cognitive response to danger, shaped by judgements about status, power, and the attitudes of others. He advises orators how to harness its persuasive power. But Aristotle opposes rhetorical manipulation and coercion. For him, rhetoric is an expertise in providing ‘proofs’ (pisteis) – justifiable grounds for conviction. Arousing fear is commended insofar as it provides rational support for the orator's conclusions. In the Politics, fear is a powerful but rational force within political life. Fear can cause stasis (faction) and revolution but can also unify citizens to support the constitution against a common threat. Understanding the cognitive and rational structure of fear equips Aristotle both to provide practical advice to orators in the Rhetoric and to explain real-world politics, constitutional change, and governance in the Politics.

Large language models (LLMs) are increasingly used to predict human emotions, but previous studies show that these models reproduce gendered emotion stereotypes. Emotion stereotypes are also tightly tied to race and skin tone (consider for example the trope of the angry black woman), but previous work has thus far overlooked this dimension. In this paper, we address this gap by introducing the first large-scale multimodal study of racial, gender, and skin-tone bias in emotion attribution, revealing how modality (text, images) and their combination shape emotion stereotypes in Multimodal LLMs (MLLMs). We evaluate four open-source MLLMs using 2.1K emotion-related events paired with 400 neutral face images across three different prompt strategies. Our findings reveal varying biases in MLLMs representations of different racial groups: models reproduce racial stereotypes across modalities, with textual cues being particularly noticeable. Models also reproduce colourist trends, with darker skin tones showing more skew. Our research highlights the need for future rigorous evaluation and mitigation strategies that account for race, colorism, and gender in MLLMs.

Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work has shown that LLMs display biases in emotion attribution along gender lines. However, unlike gender, which says little about our values, religion, as a socio-cultural system, prescribes a set of beliefs and values for its followers. Religions, therefore, cultivate certain emotions. Moreover, these rules are explicitly laid out and interpreted by religious leaders. Using emotion attribution, we explore how different religions are represented in LLMs. We find that:Major religions in the US and European countries are represented with more nuance, displaying a more shaded model of their beliefs.Eastern religions like Hinduism and Buddhism are strongly stereotyped.Judaism and Islam are stigmatized – the models’ refusal skyrocket. We ascribe these to cultural bias in LLMs and the scarcity of NLP literature on religion. In the rare instances where religion is discussed, it is often in the context of toxic language, perpetuating the perception of these religions as inherently toxic. This finding underscores the urgent need to address and rectify these biases. Our research emphasizes the crucial role emotions play in shaping our lives and how our values influence them.

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP.

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like 'When I had a serious argument with a dear person'. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications.

Anger, far from being only a personal emotion, often signals a breakdown in existing societal structures like the justice system. This does not mean we should uncritically submit to our angry impulses, but it does mean that anger can reveal larger issues in the world worthy of attention. If we banish anger from the socio-political landscape, we risk losing its insights. To defend that claim, I turn to a range of sources from ancient China and Greece—philosophy, poetry, drama, and political theory—that depict and analyze anger in a variety of situations and people. My basic claim is not that anger is simply “good” or “bad” but rather that it helps reveal and clarify our values, often pointing us towards real situations deserving of our ethical scrutiny. I situate the project amidst ongoing debates in analytic philosophy of emotions and feminist philosophy, joining the conceptual precision and clarity of the former with the latter’s attention to lived experience. Such cross-cultural, multidisciplinary conceptual analysis of anger parochializes Aristotelian understandings of anger by introducing different types and functions of anger. What role does anger play, for example, in a minister rebuking the king in an ancient Chinese court? How do Greek sources depict female anger differently than male anger, and why? By using ancient and comparative sources, I show how deeply anger is shaped by particular cultural and social structures, whilst remaining recognizably anger.

Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it. As such, whether we soothe or flame an emotion is not inconsequential. Recent work in conversational AI has focused on responding empathetically to users, validating and soothing their emotions without a real basis. This AI-aided emotional regulation can have negative consequences for users and society, tending towards a one-noted happiness defined as only the absence of "negative" emotions. We argue that we must carefully consider whether and how to respond to users' emotions.

This paper explores the ways in which Cordwainer Smith’s short story “Under Old Earth” problematizes emotions, who/what has them, and who/what is granted moral status. Most importantly, however, “Under Old Earth” questions the primacy of happiness in human society, especially where happiness is understood as the absence of other (negative) emotions. As such, “Under Old Earth” challenges the notion, widely held in contemporary ethics, that our moral obligation to one another is mediated through the goal of the attainment of happiness. Through this challenge, Smith’s short story speaks directly to the current understanding of emotion in the field of AI, and complicates what the goal of affective computing should be.

The emotion of anger has received overall negative treatment in recent moral philosophy. This article explores the gendered representations of anger in the Lienüzhuan 《列女傳》 of Liu Xiang 劉向 (77–6 BCE). It begins with a brief account of the semantic field of anger and its representation in the Lienüzhuan, focusing on three important patterns. Perhaps most important is the didactic role of anger; and how female teachers use it (or avoid it) in instructing male sons, husbands and rulers. Second is the treatment of women’s distinct strategies for addressing the effects of being the object of the anger of others. The third is the representation of female anger in accounts of female vice. The representations of anger in the LNZ provides an important alternative to views of anger as motivated by the desire for payback and status. The LNZ, like other pre-Qin texts, understands anger very differently, and focuses on other-regarding anger, based on perceptions of wrongdoing and injustice.