1,720,989 research outputs found
Prospettive della modellistica nell'età dell'incertezza
No full textNegli ultimi decenni, il tramonto del paradigma razional-comprensivo ed il profondo cambiamento della scienza che va sotto il nome di nuova “scienza della complessità” hanno favorito l’avvento di un’età dell’incertezza, segnata dalla consapevolezza dell’inadeguatezza degli strumenti di conoscenza e dalla mutazione della pianificazione, sempre più lontana dalla tradizionale concezione di un’azione lineare e ideologicamente orientata e sempre più aderente alle forme di un complesso meccanismo sociale auto-organizzato. Ci si chiede se in una simile stagione sia ancora possibile e proponibile un ruolo dei modelli di analisi territoriale che, paradossalmente, i rilevanti sviluppi tecnologici e la sconfinata disponibilità di dati e informazioni renderebbero oggi come non mai concretamente utilizzabili
Automated models for value prediction: A critical review of the debate
No full textMass appraisal techniques are used in the valuation of large groups of real estate assets. Their use involves the use of common real estate data, a single evaluation protocol and result verification tests. Given the vast amount of information they have to process, they are entrusted to automatic value prediction models. If initially these models were based on the theory of implicit marginal prices, identified through regression analysis, now they can take radically different forms thanks to the novelties brought by statistical self-learning algorithms. The algorithms of automatic learning – known as machine learning models – autonomously learn the information contained in a dataset. They are able to acquire the existing relations between the characteristics of the assets and the values of price of the goods, even when these have forms well distant from the more traditional linear relation. Each model is first trained with the data of known cases, and then tested in its ability to predict unknown values. The scientific literature has followed the evolution of the machine learning models for the prediction of the value, investigating them under more analysis profiles. The most frequently found research theme concerns the comparison of several evaluation models on the same dataset of real estate data, compared in terms of accuracy in the prediction. The research provides a critical review of the debate in all publications in which the effectiveness of new value prediction models has been empirically investigated. The models prove to be effective in their predictive capacity, less effective in their inferential capacity, i.e. to evaluate the dependence of the price phenomenon on the causes explained by the variables. The debate confirms a higher accuracy of prediction of the new models with respect to the traditional regression analysis. However, it is not possible to rank the models in order of accuracy, as the effectiveness of each model depends on the data available to it. In the face of this undeniable advantage, these models present a limit in their characteristic of black box: the valuer cannot know with certainty what values and forms the variables assume in the learning processes. This makes the models ineffective for understanding the dynamics of formation and variation of value in relation to the characteristics of the good and external agents
Development rights and territorial government: Towards urban equalization extended? | Diritti edificatori e governo del territorio: Verso una perequazione urbanistica estesa?
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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