Contextual Injustice

Contextualist treatments of clashes of intuitions can allow that two claims, apparently in conflict, can both be true. But making true utterances is far from the only thing that matters — there are often substantive normative questions about what contextual parameters are appropriate to a given conversational situation. This paper foregrounds the importance of the social power to set contextual standards, and how it relates to injustice and oppression, introducing a phenomenon I call "contextual injustice," which has to do with the unjust manipulation of conversational parameters in context-sensitive discourse. My central example applies contextualism about knowledge ascriptions to questions about knowledge regarding sexual assault allegations, but I will also discuss parallel dynamics in other examples of context-sensitive language involving politically significant terms, including gender terms. The central upshot is that the connections between language, epistemology, and social justice are very deeply interlinked

Contextual advertising

Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor. © 2012 INFORMS

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Decontextualizing contextual inversion

Contextual inversion, introduced as an analytical tool by David Lewin, is a concept of wide reach and value in music theory and analysis, at the root of neo-Riemannian theory as well as serial theory, and useful for a range of analytical applications. A shortcoming of contextual inversion as it is currently understood, however, is, as implied by the name, that the transformation has to be defined anew for each application. This is potentially a virtue, requiring the analyst to invest the transformational system with meaning in order to construct it in the first place. However, there are certainly instances where new transformational systems are continually redefined for essentially the same purposes. This paper explores some of the most common theoretical bases for contextual inversion groups and considers possible definitions of inversion operators that can apply across set class types, effectively decontextualizing contextual inversions.Accepted manuscrip

Defining contextual advantage: exploring the contextual relation between effectuation and entrepreneurial marketing for creating new markets effectually

The paper explores the behaviour of the Entrepreneur and the Effectual use of available resources mainly social capital in new market creation. The study dwells on creating a unique ‘Context’ by leveraging these resources to increase the Entrepreneurial orientation of a firm. The paper further attempts to explore whether the Contextual link between Effectuation and Entrepreneurial Marketing helps develop a ‘Contextual Advantage’, which can be used as a mean of developing a unique business model which differentiates the firm in the market. The paper hence explores contemporary theories of Entrepreneurship and Marketing namely Entrepreneurial Marketing, Effectuation and Contextual Marketing by studying their inter-relation. The nature of these theories is under-explored according to the authors and requires further investigation to evolve the field of Marketing and Entrepreneurship.N/

Contextual Outlier Interpretation

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers does not receive much attention. Interpretation is becoming increasingly important to help people trust and evaluate the developed models through providing intrinsic reasons why the certain outliers are chosen. It is difficult, if not impossible, to simply apply feature selection for explaining outliers due to the distinct characteristics of various detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, the role of contrastive contexts where outliers locate, as well as the relation between outliers and contexts, are usually overlooked in interpretation. To tackle the issues above, in this paper, we propose a novel Contextual Outlier INterpretation (COIN) method to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework compared with existing interpretation approaches