Institutional Persistence and Change in England's Common Law: 1700-1865

Institutions are widely regarded as important, even ultimate drivers of economic growth and performance. A recent mainstream of institutional economics has concentrated on the effect of persisting, often imprecisely measured institutions and on cataclysmic events as agents of noteworthy institutional change. As a consequence, institutional change without large-scale shocks has received little attention. In this dissertation I apply a complementary, quantitative-descriptive approach that relies on measures of actually enforced institutions to study institutional persistence and change over a long time period that is undisturbed by the typically studied cataclysmic events. By placing institutional change into the center of attention one can recognize different speeds of institutional innovation and the continuous coexistence of institutional persistence and change. Specifically, I combine text mining procedures, network analysis techniques and statistical approaches to study persistence and change in England’s common law over the Industrial Revolution (1700-1865). Based on the doctrine of precedent - a peculiarity of common law systems - I construct and analyze the apparently first citation network that reflects lawmaking in England. Most strikingly, I find large-scale change in the making of English common law around the turn of the 19th century - a period free from the typically studied cataclysmic events. Within a few decades a legal innovation process with low depreciation rates (1 to 2 percent) and strong past-persistence transitioned to a present-focused innovation process with significantly higher depreciation rates (4 to 6 percent) and weak past-persistence. Comparison with U.S. Supreme Court data reveals a similar U.S. transition towards the end of the 19th century. The English and U.S. transitions appear to have unfolded in a very specific manner: a new body of law arose during the transitions and developed in a self-referential manner while the existing body of law lost influence, but remained prominent. Additional findings suggest that Parliament doubled its influence on the making of case law within the first decades after the Glorious Revolution and that England’s legal rules manifested a high degree of long-term persistence. The latter allows for the possibility that the often-noted persistence of institutional outcomes derives from the actual persistence of institutions.

New developments in daily licencing practice workshop

New developments in higher education and research are having their repercussions in dailylicencing practice. Examples are; demands for perpetual access usage of licensed content incourse packs or virtual research environments text mining open access to publications. Atthe Knowledge Exchange workshop on LicencingPractice, twenty Experts discussed how these newdevelopments could be incorporated in licencing. The workshop consisted of four presentations oncurrent developments in licencing followed by threeparallel breakout sessions on the topics open access,new developments and data and text mining. This led toa lively exchange of ideas. Especially the aspect of dataand text mining provided valuable insights in how thiscould be incorporated in licencing. The Knowledge Exchange Licensing expert group willwork on how to implement the model provisions discussed. Input from the workshop was collected for a workshop with publishers to take place in March 2012 and will include these provisions in their licences. The various suggestions will be also shared with other international organisations working inthis field.

Minería de textos

Presentación para la docencia de la asignatura "Ingeniería del conocimiento biomédico y del producto, I+D en investigación traslacional del Master Universitario Investigación Traslacional y Medicina Personalizda (Transmed)de la Universidad de Granada.

Biochemical and epidemiological investigations of non-steroidal anti-inflammatory drug usage and related side effects in equids

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used in equine veterinary practice. These drugs exert their effect by inhibiting cyclooxygenase (COX) enzymes, which control prostaglandin production, a major regulator of tissue perfusion. Two isoforms of COX enzymes exist: COX-1 is physiologically present in tissues, while COX-2 is up-regulated during inflammation and has been indicated as responsible for the negative effects of an inflammatory response. Evidence suggests that NSAIDs that inhibit only COX-2, preserving the physiological function of COX-1 might have a safer profile. Studies that evaluate the effect of NSAIDs on COX enzymes are all performed under experimental conditions and none uses actual clinical patients. The biochemical investigations in this work focus on describing the effect on COX enzymes activity of flunixin meglumine and phenylbutazone, two non-selective COX inhibitors and firocoxib, a COX-2 selective inhibitor, in clinical patients undergoing elective surgery. A separate epidemiological investigation was aimed at describing the impact that the findings of biochemical data have on a large population of equids. Electronic medical records (EMRs) from 454,153 equids were obtained from practices in the United Kingdom, United States of America and Canada. Information on prevalence and indications for NSAIDs use was extracted from the EMRs via a text mining technique, improved from the literature and described and validated within this Thesis. Further the prevalence of a clinical sign compatible with NSAID toxicity, such as diarrhoea, is reported along with analysis evaluating NSAID administration in light of concurrent administration of other drugs and comorbidities. This work confirms findings from experimental settings that NSAIDs firocoxib is COX-2 selective and that flunixin meglumine and phenylbutazone are non-selective COX inhibitors and therefore their administration carries a greater risk of toxicity. However the impact of this finding needs to be interpreted with caution as epidemiological data suggest that the prevalence of toxicity is in fact small and the use of these drugs at the labelled dose is quite safe.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

Fictitious capital and the elusive quest in understanding its implications : illusions and paradoxes

This paper deals with the interaction between fictitious capital and the neoliberal model of growth and distribution, inspired by the classical economic tradition. Our renewed interest in this literature has a close connection with the recent international crisis in the capitalist economy. However, this discussion takes as its point of departure the fact that standard economic theory teaches that financial capital, in this world of increasing globalization, leads to new investment opportunities which improve levels of growth, employment, income distribution, and equilibrium. Accordingly, it is said that such financial resources expand the welfare of people and countries worldwide. Here we examine some illusions and paradoxes of such a paradigm. We show some theoretical and empirical consequences of this vision, which are quite different and have harmful constraints.

Aplicação de técnicas de mineração de textos para classificação de documentos : um estudo da automatização da triagem de denúncias na CGU

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.

Macrodynamics of economics: a bibliometric history

A history of specialties in economics since the late 1950s is constructed on the basis of a large corpus of documents from economics journals. The production of this history relies on a combination of algorithmic methods that avoid subjective assessments of the boundaries of specialties: bibliographic coupling, automated community detection in dynamic networks and text mining. these methods uncover a structuring of economics around recognizable specialties with some significant changes over the time-period covered (1956-2014). Among our results, especially noteworthy are (a) the clearcut existence of 10 families of specialties, (b) the disappearance in the late 1970s of a specialty focused on general economic theory, (c) the dispersal of the econometrics-centered specialty in the early 1990s and the ensuing importance of specific econometric methods for the identity of many specialties since the 1990s, (d) the low level of specialization of individual economists throughout the period in contrast to physicists as early as the late 1960s.

Stochastic models and graph theory for Zipf's law

In questo elaborato ci siamo occupati della legge di Zipf sia da un punto di vista applicativo che teorico. Tale legge empirica afferma che il rango in frequenza (RF) delle parole di un testo seguono una legge a potenza con esponente -1. Per quanto riguarda l'approccio teorico abbiamo trattato due classi di modelli in grado di ricreare leggi a potenza nella loro distribuzione di probabilità. In particolare, abbiamo considerato delle generalizzazioni delle urne di Polya e i processi SSR (Sample Space Reducing). Di questi ultimi abbiamo dato una formalizzazione in termini di catene di Markov. Infine abbiamo proposto un modello di dinamica delle popolazioni capace di unificare e riprodurre i risultati dei tre SSR presenti in letteratura. Successivamente siamo passati all'analisi quantitativa dell'andamento del RF sulle parole di un corpus di testi. Infatti in questo caso si osserva che la RF non segue una pura legge a potenza ma ha un duplice andamento che può essere rappresentato da una legge a potenza che cambia esponente. Abbiamo cercato di capire se fosse possibile legare l'analisi dell'andamento del RF con le proprietà topologiche di un grafo. In particolare, a partire da un corpus di testi abbiamo costruito una rete di adiacenza dove ogni parola era collegata tramite un link alla parola successiva. Svolgendo un'analisi topologica della struttura del grafo abbiamo trovato alcuni risultati che sembrano confermare l'ipotesi che la sua struttura sia legata al cambiamento di pendenza della RF. Questo risultato può portare ad alcuni sviluppi nell'ambito dello studio del linguaggio e della mente umana. Inoltre, siccome la struttura del grafo presenterebbe alcune componenti che raggruppano parole in base al loro significato, un approfondimento di questo studio potrebbe condurre ad alcuni sviluppi nell'ambito della comprensione automatica del testo (text mining).

Apprendimento non supervisionato di rappresentazioni e legami di similarità tra eventi menzionati nella letteratura biomedica

L'estrazione automatica degli eventi biomedici dalla letteratura scientifica ha catturato un forte interesse nel corso degli ultimi anni, dimostrandosi in grado di riconoscere interazioni complesse e semanticamente ricche espresse all'interno del testo. Purtroppo però, esistono davvero pochi lavori focalizzati sull'apprendimento di embedding o di metriche di similarità per i grafi evento. Questa lacuna lascia le relazioni biologiche scollegate, impedendo l'applicazione di tecniche di machine learning che potrebbero dare un importante contributo al progresso scientifico. Approfittando dei vantaggi delle recenti soluzioni di deep graph kernel e dei language model preaddestrati, proponiamo Deep Divergence Event Graph Kernels (DDEGK), un metodo non supervisionato e induttivo in grado di mappare gli eventi all'interno di uno spazio vettoriale, preservando le loro similarità semantiche e strutturali. Diversamente da molti altri sistemi, DDEGK lavora a livello di grafo e non richiede nè etichette e feature specifiche per un determinato task, nè corrispondenze note tra i nodi. A questo scopo, la nostra soluzione mette a confronto gli eventi con un piccolo gruppo di eventi prototipo, addestra delle reti di cross-graph attention per andare a individuare i legami di similarità tra le coppie di nodi (rafforzando l'interpretabilità), e impiega dei modelli basati su transformer per la codifica degli attributi continui. Sono stati fatti ampi esperimenti su dieci dataset biomedici. Mostriamo che le nostre rappresentazioni possono essere utilizzate in modo efficace in task quali la classificazione di grafi, clustering e visualizzazione e che, allo stesso tempo, sono in grado di semplificare il task di semantic textual similarity. Risultati empirici dimostrano che DDEGK supera significativamente gli altri modelli che attualmente detengono lo stato dell'arte.

Generazione automatica di una knowledge base con applicazione ai Sustainable Development Goals

Questa tesi di laurea compie uno studio sull’ utilizzo di tecniche di web crawling, web scraping e Natural Language Processing per costruire automaticamente un dataset di documenti e una knowledge base di coppie verbo-oggetto utilizzabile per la classificazione di testi. Dopo una breve introduzione sulle tecniche utilizzate verrà presentato il metodo di generazione, prima in forma teorica e generalizzabile a qualunque classificazione basata su un insieme di argomenti, e poi in modo specifico attraverso un caso di studio: il software SDG Detector. In particolare quest ultimo riguarda l’applicazione pratica del metodo esposto per costruire una raccolta di informazioni utili alla classificazione di documenti in base alla presenza di uno o più Sustainable Development Goals. La parte relativa alla classificazione è curata dal co-autore di questa applicazione, la presente invece si concentra su un’analisi di correttezza e performance basata sull’espansione del dataset e della derivante base di conoscenza.

Sentiment analysis: la guerra russo-ucraina su twitter

Nel corso dell’elaborato verranno utilizzate tecniche e strumenti di analisi automatica di dati aventi carattere testuale. Lo scopo del lavoro di tesi consisterà nel condurre text mining e sentiment analysis su dei messaggi al fine di comprenderne il significato, con interesse particolare sulle emozioni ed i sentimenti in essi contenuti per riuscire ad estrapolare informazioni di interesse.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

The cyanide process of gold extraction: a text-book for the use of mining students, metallurgists, and cyanide operators.

Mode of access: Internet.