Minor Moves: Growth, Fugitivity, and Children's Physical Movement

From tendencies to reduce the Underground Railroad to the imperative "follow the north star" to the iconic images of Ruby Bridges' 1960 "step forward" on the stairs of William Frantz Elementary School, America prefers to picture freedom as an upwardly mobile development. This preoccupation with the subtractive and linear force of development makes it hard to hear the palpable steps of so many truant children marching in the Movement and renders illegible the nonlinear movements of minors in the Underground. Yet a black fugitive hugging a tree, a white boy walking alone in a field, or even pieces of a discarded raft floating downstream like remnants of child's play are constitutive gestures of the Underground's networks of care and escape. Responding to 19th-century Americanists and cultural studies scholars' important illumination of the child as central to national narratives of development and freedom, "Minor Moves" reads major literary narratives not for the child and development but for the fugitive trace of minor and growth.

In four chapters, I trace the physical gestures of Nathaniel Hawthorne's Pearl, Harriet Beecher Stowe's Topsy, Harriet Wilson's Frado, and Mark Twain's Huck against the historical backdrop of the Fugitive Slave Act and the passing of the first compulsory education bills that made truancy illegal. I ask how, within a discourse of independence that fails to imagine any serious movements in the minor, we might understand the depictions of moving children as interrupting a U.S. preoccupation with normative development and recognize in them the emergence of an alternative imaginary. To attend to the movement of the minor is to attend to what the discursive order of a development-centered imaginary deems inconsequential and what its grammar can render only as mistakes. Engaging the insights of performance studies, I regard what these narratives depict as childish missteps (Topsy's spins, Frado's climbing the roof) as dances that trouble the narrative's discursive order. At the same time, drawing upon the observations of black studies and literary theory, I take note of the pressure these "minor moves" put on the literal grammar of the text (Stowe's run-on sentences and Hawthorne's shaky subject-verb agreements). I regard these ungrammatical moves as poetic ruptures from which emerges an alternative and prior force of the imaginary at work in these narratives--a force I call "growth."

Reading these "minor moves" holds open the possibility of thinking about a generative association between blackness and childishness, one that neither supports racist ideas of biological inferiority nor mandates in the name of political uplift the subsequent repudiation of childishness. I argue that recognizing the fugitive force of growth indicated in the interplay between the conceptual and grammatical disjunctures of these minor moves opens a deeper understanding of agency and dependency that exceeds notions of arrested development and social death. For once we interrupt the desire to picture development (which is to say the desire to picture), dependency is no longer a state (of social death or arrested development) of what does not belong, but rather it is what Édouard Glissant might have called a "departure" (from "be[ing] a single being"). Topsy's hard-to-see pick-pocketing and Pearl's running amok with brown men in the market are not moves out of dependency but indeed social turns (a dance) by way of dependency. Dependent, moving and ungrammatical, the growth evidenced in these childish ruptures enables different stories about slavery, freedom, and childishness--ones that do not necessitate a repudiation of childishness in the name of freedom, but recognize in such minor moves a fugitive way out.

Finding our way through phenotypes.

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Quantitative genetics of CTCF binding reveal local sequence effects and different modes of X-chromosome association.

Associating genetic variation with quantitative measures of gene regulation offers a way to bridge the gap between genotype and complex phenotypes. In order to identify quantitative trait loci (QTLs) that influence the binding of a transcription factor in humans, we measured binding of the multifunctional transcription and chromatin factor CTCF in 51 HapMap cell lines. We identified thousands of QTLs in which genotype differences were associated with differences in CTCF binding strength, hundreds of them confirmed by directly observable allele-specific binding bias. The majority of QTLs were either within 1 kb of the CTCF binding motif, or in linkage disequilibrium with a variant within 1 kb of the motif. On the X chromosome we observed three classes of binding sites: a minority class bound only to the active copy of the X chromosome, the majority class bound to both the active and inactive X, and a small set of female-specific CTCF sites associated with two non-coding RNA genes. In sum, our data reveal extensive genetic effects on CTCF binding, both direct and indirect, and identify a diversity of patterns of CTCF binding on the X chromosome.