Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

Universality caused: the case of renormalization group explanation

Recently, many have argued that there are certain kinds of abstract mathematical explanations that are noncausal. In particular, the irrelevancy approach suggests that abstracting away irrelevant causal details can leave us with a noncausal explanation. In this paper, I argue that the common example of Renormalization Group explanations of universality used to motivate the irrelevancy approach deserves more critical attention. I argue that the reasons given by those who hold up RG as noncausal do not stand up to critical scrutiny. As a result, the irrelevancy approach and the line between casual and noncausal explanation deserves more scrutiny

Negative Epistemic Exemplars

In this chapter, we address the roles that exemplars might play in a comprehensive response to epistemic injustice. Fricker defines epistemic injustices as harms people suffer specifically in their capacity as (potential) knowers. We focus on testimonial epistemic injustice, which occurs when someone’s assertoric speech acts are systematically met with either too little or too much credence by a biased audience. Fricker recommends a virtue­theoretic response: people who do not suffer from biases should try to maintain their disposition towards naive testimonial justice, and those who find themselves already biased should cultivate corrective testimonial justice by systematically adjusting their credence in testimony up or down depending on whether they are hearing from someone whom they may be biased against or in favor of. We doubt that the prominent admiration­emulation model of exemplarism will be much use in this connection, so we propose two ways of learning from negative exemplars to better conduct one’s epistemic affairs. In the admiration­emulation model, both the identification of what a virtue is and the cultivation of virtues identified thusly proceed through the admiration of virtuous exemplars. We show that this model has serious flaws and argue for two alternatives: the envy­agonism model and the ambivalence­avoidance model

Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

First Grade Teachers’ Perspectives on Using Nonfiction Texts in Guided Reading and Read-Aloud Lessons

This research assessed first-grade teachers’ perspectives on using nonfiction text during guided reading and read-aloud lessons. Three teachers were all surveyed, observed, interviewed, and their classroom libraries were inventoried. Later the study revealed teachers’ perspectives on using nonfiction text. The findings showed there was a positive correlation between teachers’ increase in confidence and their use of the texts, and that teachers who had a high number of nonfiction texts in their classrooms incorporated the texts more often. The research gave implications for student learning which were students benefit from being taught about nonfiction text structure and nonfiction text engages students. It is recommended that teachers require education on nonfiction text and students need to be engaged with nonfiction text

Nasty Women: Television Portrayals of Societal Anxieties toward Female Leaders

Historically, women have been excluded from leadership positions around the world, while instead men occupy the highest positions of power in society. The lack of female leadership is especially prevalent in the United States, where there has never been a female president, and the majority of high political offices are still held by men. In a similar manner, women have also been excluded from the sphere of comedy throughout history. Women have constantly had to deal with the assertion that women are not funny. This double exclusion from both leadership and comedy has led to the development of my concept of the nasty woman, who is a woman who seeks to hold a leadership position in a way that patriarchal society deems threatening to the status quo. The nasty woman does not conform to societal expectations because she seeks to ensure that women do not have to only follow the traditional path that are set for them, paths that exclude leadership roles. Although there are many examples of nasty women in the real world, I chose to analyze Selina Meyer from Veep and Leslie Knope from Parks and Recreation as portrayals of the societal anxieties about nasty women, or female leaders, on comedy television shows. Selina acts as an example of a terrible leader, while Leslie acts as an example of an amazing leader. However, both of these women still cause society to experience anxiety because of their decisions and actions as female leaders. The fear of these women exists because, by seeking to obtain leadership positions in society, they are going against the traditional roles that society expects women to play. Overall, the portrayals of both Selina and Leslie represent three main anxieties that society has about female leaders: the fear that women are incompetent, uncontrollable, and altogether bad leaders, the fear that women leaders will abandon traditional feminine roles, and the fear that female leaders will castrate the men around them. With the portrayal of these ideas in comedy shows, the representations of Selina and Leslie emphasize the ridiculous and hilarity of these negative expectations of female leaders in a way that undermines the negative perceptions of female leaders in the United States today

Reclaiming subjecthood : education and the art of quality experience

This essay draws on writings in education, philosophy, psychology, neuroscience, and social work to articulate values for educational practice. It looks at individual development, relationship, and art as three fundamental areas of quality experience and education. Within and across these arenas, three themes repeatedly surface: attention, critical mindedness, and the balance of process and product

Vectors of epistemic insecurity

Epistemologists have addressed a variety of modal epistemic standings, such as sensitivity, safety, risk, and epistemic virtue. These concepts mark out the ways that beliefs can fail to track the truth, articulate the conditions needed for knowledge, and indicate ways to become a better epistemic agent. However, it is our contention that current ways of carving up epistemic modality ignore the complexities that emerge when individuals are embedded within a community and listening to a variety of sources, some of whom are intentionally engaged in deception or bullshit. In this context we want our beliefs to be secure. In this paper we translate the epistemic modal standing of safety into a framework appropriate for social epistemology and argue for the importance of epistemic network-security and belief-security to be added to this framework. We discuss the virtues that are salient for promoting network-security and the vices that undermine it. In particular, we highlight monitoring, adjusting, and restructuring virtues and vices. Importantly, each of these vices can be other-regarding or self-regarding. For example, one tempting way of dealing with insecurity within a network is to completely cut oneself off from biased sources. However, we argue that this is a self-regarding restructuring vice because it closes oneself off from opportunities for epistemic growth. By contrast, an other-regarding restructuring vice would be to cut off others from hearing from sources of information that would make their network more secure

A Year in, Americans Still Support Ukraine

A majority of the US public continues to back current military and financial aid to Kyiv, recent polls find.The war in Ukraine is about to enter its second year, with rumors of a new Russian offensive being planned to mark the occasion. At the same time, US headlines have warned that some congressional support for US aid to Ukraine may be sliding, and several polls have shown that American public support for continuing to assist Ukraine "for as long as it takes" has slipped from the very high levels seen at the outset of the conflict. But these analyses often overlook the fact that Americans are still paying attention to the conflict one year into it, and their support for current military and financial aid to Kyiv remains at majority levels

Model Explanation versus Model-Induced Explanation

Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations (short: ME). But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’ (MIE). Second, we study three paradigmatic cases of alleged ME. We argue that all of them are MIE, upon closer examination. Third, we argue that this undermines the building consensus that model explanations are special explanations that, e.g., challenge the factivity of explanation. Instead, it suggests that what is special about models in science is the epistemology behind how models induce explanations