Bureaucracy and Pro-poor Change

This paper takes a political economy perspective in analysing the nature and causes on the decline in bureaucratic conduct. Section 1 lays out the details of this structure. Based on a logical model which places the bureaucracy within the larger context of the objective function of the state, the nature of the political process, the degree of centralisation and fragmentation of the bureaucratic structure and processes for monitoring and accountability of the bureaucracy, this model provides the basis for subsequent analysis. Section 2 provides a historical overview with regard to changes in the bureaucratic and political structure and the impact it had on the above mentioned balance between bureaucratic conduct and political compulsions. Section 3 then analyses the consequences on service delivery that this systematic weakening of the bureaucratic structure has had. Section 4 then critically assesses some of the recent attempts at bureaucratic reform in the light of the framework developed in Section 1. The conclusion then summarises the paper and draws implications for pro-poor change of the structure and conduct of the bureaucratic structure in PakistanPoverty, Poor, Bureaucracy

Bureaucracy and Pro-poor Change

Based on the premise that a functioning state is a necessary pre-requisite for pro-poor change, it is critical to investigate the role of the bureaucracy as a key catalyst in this process. Weber (1968) ascribes bureaucracies to be anchors of the modern nation state as their conduct is based on rational-legal norms. Bureaucracies, according to this ideal type, temper the populist urges of politicians who wish to execute policy unencumbered by rules and procedures. State success or failure in many cases, therefore, can be gauged by the degree to which this tensionbetween the rules based bureaucratic form of administration and populist politicsis resolved. Prognosis on pro-poor change in the light of the present and anticipated balance between bureaucratic procedures and political compulsions is thus an important area of inquiry. There is consensus that the disconnect between policy formulation and execution in Pakistan has widened considerably in the last three decades or so. And this is in spite of the fact of the generally acclaimed view that Pakistan inherited a well functioning and competent bureaucracy from the British Raj [Braibanti (1966)]. While part of the blame for this disconnect can be ascribed to incoherence in policy formulation on the part of the political leadershipboth civil and militarybut bureaucratic malfeasance, incompetence and corruption have been critical factors in the level of governance declining over time. This paper takes a political economy perspective in analysing the nature and causes on the decline in bureaucratic conduct. Section 1 lays out the details of this structure. Based on a logical model which places the bureaucracy within the larger context of the objective function of the state, the nature of the political process, the degree of centralisation and fragmentation of the bureaucratic structure and processes for monitoring and accountability of the bureaucracy, this model provides the basis for subsequent analysis. Section 2 provides a historical overview with regard to changes in the bureaucratic and political structure and the impact it had on the above mentioned balance between bureaucratic conduct and political compulsions. Section 3 then analyses the consequences on service delivery that this systematic weakening of the bureaucratic structure has had. Section 4 then critically assesses some of the recent attempts at bureaucratic reform in the light of the framework developed in Section 1. The conclusion then summarises the paper and draws implications for pro-poor change of the structure and conduct of the bureaucratic structure in Pakistan.bureaucracy, pro-poor change, consequences on service

A distributional and syntactic approach to fine-grained opinion mining

This thesis contributes to a larger social science research program of analyzing the diffusion of IT innovations. We show how to automatically discriminate portions of text dealing with opinions about innovations by finding {source, target, opinion} triples in text. In this context, we can discern a list of innovations as targets from the domain itself. We can then use this list as an anchor for finding the other two members of the triple at a ``fine-grained'' level---paragraph contexts or less. We first demonstrate a vector space model for finding opinionated contexts in which the innovation targets are mentioned. We can find paragraph-level contexts by searching for an ``expresses-an-opinion-about'' relation between sources and targets using a supervised model with an SVM that uses features derived from a general-purpose subjectivity lexicon and a corpus indexing tool. We show that our algorithm correctly filters the domain relevant subset of subjectivity terms so that they are more highly valued. We then turn to identifying the opinion. Typically, opinions in opinion mining are taken to be positive or negative. We discuss a crowd sourcing technique developed to create the seed data describing human perception of opinion bearing language needed for our supervised learning algorithm. Our user interface successfully limited the meta-subjectivity inherent in the task (``What is an opinion?'') while reliably retrieving relevant opinionated words using labour not expert in the domain. Finally, we developed a new data structure and modeling technique for connecting targets with the correct within-sentence opinionated language. Syntactic relatedness tries (SRTs) contain all paths from a dependency graph of a sentence that connect a target expression to a candidate opinionated word. We use factor graphs to model how far a path through the SRT must be followed in order to connect the right targets to the right words. It turns out that we can correctly label significant portions of these tries with very rudimentary features such as part-of-speech tags and dependency labels with minimal processing. This technique uses the data from the crowdsourcing technique we developed as training data. We conclude by placing our work in the context of a larger sentiment classification pipeline and by describing a model for learning from the data structures produced by our work. This work contributes to computational linguistics by proposing and verifying new data gathering techniques and applying recent developments in machine learning to inference over grammatical structures for highly subjective purposes. It applies a suffix tree-based data structure to model opinion in a specific domain by imposing a restriction on the order in which the data is stored in the structure

Building an IT Taxonomy with Co-occurrence Analysis, Hierarchical Clustering, and Multidimensional Scaling

Different information technologies (ITs) are related in complex ways. How can the relationships among a large number of ITs be described and analyzed in a representative, dynamic, and scalable way? In this study, we employed co-occurrence analysis to explore the relationships among 50 information technologies discussed in six magazines over ten years (1998-2007). Using hierarchical clustering and multidimensional scaling, we have found that the similarities of the technologies can be depicted in hierarchies and two-dimensional plots, and that similar technologies can be classified into meaningful categories. The results imply reasonable validity of our approach for understanding technology relationships and building an IT taxonomy. The methodology that we offer not only helps IT practitioners and researchers make sense of numerous technologies in the iField but also bridges two related but thus far largely separate research streams in iSchools - information management and IT management

DeepColor: Reinforcement Learning optimizes information efficiency and well-formedness in color name partitioning

As observed in the World Color Survey (WCS), some universal properties can be identified in color naming schemes over a large number of languages. For example, Regier, Kay, and Khetrapal (2007) and Regier, Kemp, and Kay (2015); Gibson et al. (2017) recently explained these universal patterns in terms of near optimal color partitions and information theoretic measures of efficiency of communication. Here, we introduce a computational learning framework with multi-agent systems trained by reinforcement learning to investigate these universal properties. We compare the results with Regier et al. (2007, 2015) and show that our model achieves excellent quantitative agreement. This work introduces a multi-agent reinforcement learning framework as a powerful and versatile tool to investi- gate such semantic universals in many domains and contribute significantly to central questions in cognitive science

A reinforcement-learning approach to efficient communication

We present a multi-agent computational approach to partitioning semantic spaces using reinforcement-learning (RL). Two agents communicate using a finite linguistic vocabulary in order to convey a concept. This is tested in the color domain, and a natural reinforcement learning mechanism is shown to converge to a scheme that achieves a near-optimal trade-off of simplicity versus communication efficiency. Results are presented both on the communication efficiency as well as on analyses of the resulting partitions of the color space. The effect of varying environmental factors such as noise is also studied. These results suggest that RL offers a powerful and flexible computational framework that can contribute to the development of communication schemes for color names that are near-optimal in an information-theoretic sense and may shape color-naming systems across languages. Our approach is not specific to color and can be used to explore cross-language variation in other semantic domains

Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and have more narrativity compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and have more narrativity than stories generated with the current state-of-the-art model.Comment: Paper accepted by Transactions of the Association for Computational Linguistics (TACL). This is a pre-MIT Press publication version. 15 pages, 6 figure

An Exploration of Semantic Features in an Unsupervised Thematic Fit Evaluation Framework

Thematic fit is the extent to which an entity fits a thematic role in the semantic frame of an event, e.g., how well humans would rate “knife” as an instrument of an event of cutting. We explore the use of the SENNA semantic role-labeller in defining a distributional space in order to build an unsupervised model of event-entity thematic fit judgements. We test a number of ways of extracting features from SENNA-labelled versions of the ukWaC and BNC corpora and identify tradeoffs. Some of our Distributional Memory models outperform an existing syntax-based model (TypeDM) that uses hand-crafted rules for role inference on a previously tested data set. We combine the results of a selected SENNA-based model with TypeDM’s results and find that there is some amount of complementarity in what a syntactic and a semantic model will cover. In the process, we create a broad-coverage semantically-labelled corpus