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Reconceptualizing Field Research  

Diana Kapiszewski, Lauren M. MacLean, and Benjamin L. Read

Generations of political scientists have set out for destinations near and far to pursue field research. Even in a digitally networked era, the researcher’s personal presence and engagement with the field context continue to be essential. Yet exactly what does fieldwork mean, what is it good for, and how can scholars make their time in the field as reflective and productive as possible? Thinking of field research in broad terms—as leaving one’s home institution to collect information, generate data, and/or develop insights that significantly inform one’s research—reveals that scholars of varying epistemological commitments, methodological bents, and substantive foci all engage in fieldwork. Moreover, they face similar challenges, engage in comparable practices, and even follow similar principles. Thus, while every scholar’s specific project is unique, we also have much to learn from each other. In preparing for and conducting field research, political scientists connect the high-level fundamentals of their research design with the practicalities of day-to-day inquiry. While in the field, they take advantage of the multiplicity of opportunities that the field setting provides and often triangulate by cross-checking among different perspectives or data sources. To a large extent, they do not regard initial research design decisions as final; instead, they iteratively update concepts, hypotheses, the research question itself, and other elements of their projects—carefully justifying these adaptations—as their fieldwork unfolds. Incorporating what they are learning in a dynamic and ongoing fashion, while also staying on task, requires both flexibility and discipline. Political scientists are increasingly writing about the challenges of special types of field environments (such as authoritarian regimes or conflict settings) and about issues of positionality that arise from their own particular identities interacting with those of the people they study or with whom they work. So too, they are grappling with what it means to conduct research in a way that aligns with their ethical commitments, and what the possibilities and limits of research transparency are in relation to fieldwork. In short, political scientists have joined other social scientists in undertaking critical reflection on what they do in the field—and this self-awareness is itself a hallmark of high-quality research.



Derek Attridge

The term singularity has been put to a variety of uses by philosophers and literary theorists with a limited degree of consistency among them. It is very often contrasted with one or more other terms which might seem to be synonyms, such as particular and individual, and its relation to universality and generality is frequently discussed. Although the term itself is not an important one for Kant, his discussion in the Critique of Judgment of the peculiar nature of aesthetic or reflective judgment marks the beginning of a long history of philosophical attention to the artwork as a singular entity resistant to analysis and the experience of art as unamenable to explanation. Some philosophical deployments of the concept of singularity stress uniqueness, self-sufficiency or transcendence (Martin Heidegger, Gilles Deleuze, Hans-Georg Gadamer); others see singularity as self-divided and existing only in relation to other singularities (Jean-Luc Nancy) or to generalities (Jacques Derrida). Singularity is sometimes understood as an event rather than an entity (Deleuze, Jean-François Lyotard, Nancy, Derrida); for some thinkers, it is closely connected with community (Giorgio Agamben, Nancy). For Derrida, the most influential of these philosophers for literary studies on this topic, singularity is inseparable from iterability; a mark or sign is able to remain the same through history and in various realizations if it is able to change with each new context in which it appears. As a term in literary theory, singularity is usually regarded as a distinctive quality of the literary work, combining as it does a sense of the work’s uniqueness with its participation in general and generic codes and norms. The reader’s encounter with the singularity of the work is an encounter with otherness that necessitates a change in his or her frameworks of understanding and feeling; every such reading is singular in that the reader and the context of reading will always be different. Iterability is a condition of literary singularity: works retain their identity only because they are open to change.


Mixed Frequency Models  

Eric Ghysels

The majority of econometric models ignore the fact that many economic time series are sampled at different frequencies. A burgeoning literature pertains to econometric methods explicitly designed to handle data sampled at different frequencies. Broadly speaking these methods fall into two categories: (a) parameter driven, typically involving a state space representation, and (b) data driven, usually based on a mixed-data sampling (MIDAS)-type regression setting or related methods. The realm of applications of the class of mixed frequency models includes nowcasting—which is defined as the prediction of the present—as well as forecasting—typically the very near future—taking advantage of mixed frequency data structures. For multiple horizon forecasting, the topic of MIDAS regressions also relates to research regarding direct versus iterated forecasting.


Iterative Meaning in Mandarin Chinese: Linguistic Factors and its Processing  

Huei-ling Lai and Yao-Ying Lai

Sentential meaning that emerges compositionally is not always transparent as one-to-one mapping from syntactic structure to semantic representation; oftentimes, the meaning is underspecified (morphosyntactically unsupported), not explicitly conveyed via overt linguistic devices. Compositional meaning is obtained during comprehension. The associated issues are explored by examining linguistic factors that modulate the construal of underspecified iterative meaning in Mandarin Chinese (MC). In this case, the factors include lexical aspect of verbs, the interval-lengths denoted by post-verbal durative adverbials, and boundary specificity denoted by preverbal versus post-verbal temporal adverbials. The composition of a punctual verb (e.g., jump, poke) with a durative temporal adverbial like Zhangsan tiao-le shi fenzhong. Zhangsan jump-LE ten minute ‘Zhangsan jumped for ten minutes’ engenders an iterative meaning, which is morphosyntactically absent yet fully understood by comprehenders. Contrastively, the counterpart involving a durative verb (e.g., run, swim) like Zhangsan pao-le shi fenzhong Zhangsan run-LE ten minute ‘Zhangsan ran for ten minutes’ engenders a continuous reading with identical syntactic structure. Psycholinguistically, processing such underspecified meaning in real time has been shown to require greater effort than the transparent counterpart. This phenomenon has been attested cross-linguistically; yet how it is manifested in MC, a tenseless language, remains understudied. In addition, durative temporal adverbials like yizhi/buduandi ‘continuously,’ which appear preverbally in MC, also engender an iterative meaning when composed with a punctual verb like Zhangsan yizhi/buduandi tiao. Zhangsan continuously jump ‘Zhangsan jumped continuously.’ Crucially, unlike the post-verbal adverbials that encode specific boundaries for the denoted intervals, these preverbal adverbials refer to continuous time spans without specific endpoints. The difference in boundary specificity between the two adverbial types, while both being durative, is hypothesized to modulate the processing profiles of aspectual comprehension. Results of the online (timed) questionnaire showed (a) an effect of boundary specificity: sentences with post-verbal adverbials that encode [+specific boundary] were rated lower in the naturalness-rating task and induced longer response time (RT) in iterativity judgements, as compared to preverbal adverbials that encode [−specific boundary]; (b) in composition with post-verbal adverbials that are [+specific boundary], sentences involving durative verbs elicited lower rating scores and longer RT of iterativity judgements than the counterpart involving punctual verbs. These suggest that the comprehension of underspecified iterative meaning is modulated by both cross-linguistically similar parameters and language-specific systems of temporal reference, by which MC exhibits a typological difference in processing profiles. Overall, the patterns are consistent with the Context-Dependence approach to semantic underspecification: comprehenders compute the ultimate reading (iterative versus continuous) by taking both the sentential and extra-sentential information into consideration in a given context.


Sparse Grids for Dynamic Economic Models  

Johannes Brumm, Christopher Krause, Andreas Schaab, and Simon Scheidegger

Solving dynamic economic models that capture salient real-world heterogeneity and nonlinearity requires the approximation of high-dimensional functions. As their dimensionality increases, compute time and storage requirements grow exponentially. Sparse grids alleviate this curse of dimensionality by substantially reducing the number of interpolation nodes, that is, grid points needed to achieve a desired level of accuracy. The construction principle of sparse grids is to extend univariate interpolation formulae to the multivariate case by choosing linear combinations of tensor products in a way that reduces the number of grid points by orders of magnitude relative to a full tensor-product grid and doing so without substantially increasing interpolation errors. The most popular versions of sparse grids used in economics are (dimension-adaptive) Smolyak sparse grids that use global polynomial basis functions, and (spatially adaptive) sparse grids with local basis functions. The former can economize on the number of interpolation nodes for sufficiently smooth functions, while the latter can also handle non-smooth functions with locally distinct behavior such as kinks. In economics, sparse grids are particularly useful for interpolating the policy and value functions of dynamic models with state spaces between two and several dozen dimensions, depending on the application. In discrete-time models, sparse grid interpolation can be embedded in standard time iteration or value function iteration algorithms. In continuous-time models, sparse grids can be embedded in finite-difference methods for solving partial differential equations like Hamilton-Jacobi-Bellman equations. In both cases, local adaptivity, as well as spatial adaptivity, can add a second layer of sparsity to the fundamental sparse-grid construction. Beyond these salient use-cases in economics, sparse grids can also accelerate other computational tasks that arise in high-dimensional settings, including regression, classification, density estimation, quadrature, and uncertainty quantification.