Regulatory Review: Presidential Control Through Selective Communication and Institutional Conflict

The range and quantity of government activity has grown exponentially over the course of this century. In response, an "institutional Presidency" has developed. Today’s President is at the locus of a network of relationships designed to increase his capacity to influence the flow of events. This paper examines a small sample of those relationships: His relationship with the Office of Information and Regulatory Affairs (OIRA), a division of the Office of Management and Budget (OMB) that reviews regulations. OIRA was one of the key instruments used by the Ronald Reagan and George H.W. Bush Administrations to limit regulation. The operational question of this paper is: What happened to the OIRA-White House relationship when there was a change in party and policy preferences in the White House in 1993? That is, how does a relatively pro-regulatory White House use OIRA? To answer this question, this paper examines panel data on the policy preferences and social interaction patterns of OIRA members, time series data on the disposition of reviewed regulations, and structured interviews with members of OIRA. Analysis of these data indicates that the review process institutionalizes conflict between the OMB and agencies, resulting in appeals to the President when actors believe that the President will support their views. Thus, the George H.W. Bush and Bill Clinton Administrations economized on their limited ability to monitor the Executive Branch by focusing their attention and political support on members of OIRA with compatible viewpoints

ConStance: Modeling Annotation Contexts to Improve Stance Classification

Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model's interpretable parameters shed light on the effects of each context.Comment: To appear at EMNLP 201

Semantic-driven matchmaking of web services using case-based reasoning

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services

Electrophoretic separation of human kidney cells at zero gravity

Electrophoretic isolation of cells results in a loss of resolution power caused by the sedimentation of the cells in the media. The results of an experiment to extract urokinase from human embryos during the Apollo Soyuz mission are presented and discussed

Explaining the Diffusion of Web-Based Communication Technology among Congressional Offices: A Natural Experiment using State Delegations

Do legislators learn to use new communication technologies from each other? Using data from the official homepages of members of the U.S. House of Representatives, we test whether web-based communication technology diffuses through congressional state delegations. We use a natural experimental design that exploits ignorable state boundaries to distinguish between causal diffusion processes and spatial heterogeneity. Using nonlinear conditional autoregressive models for the statistical test, we find that web communication technology practices are weakly driven by communication within state delegations, and with the effect slightly more pronounced among Democrats than among Republicans