1,720,957 research outputs found
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
Drivers and Barriers of Sustainable Behaviours Among Young Generations in a Climate-Vulnerable Italian City
Full text linkSustainable behaviours are essential for addressing climate change, particularly as extreme weather events intensify globally. Identifying the factors that drive or hinder these behaviours is crucial for developing effective interventions. However, existing behavioural models often overlook cultural, social, and contextual influences shaping sustainable actions, especially in climate-vulnerable regions.
The COM-B model is a behavioural framework that explains behaviour change through the interplay of Capability, Opportunity, and Motivation, which together determine whether a Behaviour can occur. In this study, we apply it—among its first uses in climate change research—to analyze the determinants influencing sustainable behaviours. Conducted in Chiavari, a Ligurian city prone to floods, the research involved 470 secondary school students (aged 15–17) and 117 young adults (aged 18–35). Results show that young adults with direct experience of extreme events exhibit greater climate awareness (90% vs. 80% of students) and a higher tendency to engage in sustainable behaviours, while students demonstrate a stronger belief in the effectiveness of collective action.
The analysis highlights moderate positive correlations between motivation and sustainable behaviour, as well as between capability and both motivation and behaviour, emphasizing capability’s key role in fostering motivatio
Sentinel-2 time series and machine learning models for fuel type classification in Mediterranean vegetation
No full textLAUREA MAGISTRALEGli incendi boschivi (wildfires o forest fires) sono tra i fenomeni più rilevanti per gli ecosistemi dell’Europa mediterranea, in particolare nelle aree più soggette agli incendi, come la Sardegna, in Italia, dove il rischio di incendio è aggravato dalla diversità dei tipi di combustibile. Il rischio incendi nei paesi europei che si affacciano sul bacino del Mediterraneo (Portogallo, Spagna, Italia e Greci) è in aumento anche a causa delle condizioni climatiche più aride (siccità e temperature in aumento). L e strategie di gestione degli incendi si basano sulla conoscenza accurata dei tipi di combustibile, per prevedere il comportamento del fuoco e implementare misure di mitigazione del rischio. Questa tesi propone un approccio metodologico basato su algoritmi di machine learning (ML) applicati alle serie temporali dei dati satellitari Copernicus Sentinel-2 per classificare i tipi di combustibile in Sardegna; i modelli testati e proposti in questa tesi si basano su un dataset di parametri spazialmente distribuiti che caratterizzano lo stato della vegetazione e del territorio (serie temporali di indici di vegetazione, topografia, altezza della canopy e FAPAR) per la generazione di una mappa di classi di combustibile a scala regionale alla risoluzione spaziale di 10m (dimensione del pixel dei dati Sentinel-2). La classificazione proposta di tipo supervisionato, si basa anche su un dataset di campioni raccolto per fotointerpretazione e utilizzando un’applicazione web sviluppata da alcuni istituti del Consiglio Nazionale delle Ricerche (CNR). Lo studio ha adattato il sistema gerarchico di classificazione dei combustibili del progetto europeo H2020 FirEUrisk alla specifica vegetazione e topografia mediterranea della Sardegna. Durante il lavoro di tesi presso il CNR, è stato raccolto un esteso dataset per l’addestramento e la validazione di due modelli: Gradient Tree Boosting (GTB) e Support Vector Machine (SVM) implementati in Python. L’implementazione ha richiesto lo sviluppo di codici/script Py utilizzando Jupyter Notebook, un'applicazione web open-source per il calcolo interattivo. I modelli di classificazione hanno prodotto risultati in fase di test promettenti con livelli di overall accuracy del 79% e del 76% per i modelli SVM e GTB, rispettivamente. Questi risultati dimostrano l'efficacia dei modelli nel distinguere i tipi di combustibile, cruciali per creare mappe ad alta risoluzione dei tipi di combustibile. Tuttavia sono state evidenziate criticità nella distinzione di alcune classi di combustibile come, per esempio, le classi arbustive ed erbacee. I risultati di questa tesi sono di rilevante interesse per il progetto FirEUrisk e per la generazione di una mappa a scala regionale per una delle aree pilota del progetto: la Sardegna.Wildfires pose a significant threat to Mediterranean European ecosystems, particularly in highly fire-prone areas such as the island of Sardinia, Italy, where prolonged dry periods and extensive fuel accumulation amplify their frequency and intensity. Fire risk in European Mediterranean countries (Portugal, Spain, Italy, and Greece) is increasing due to more arid climatic conditions, such as drought and rising temperatures. Effective fire management strategies rely on accurate fuel type classification, which is essential for predicting fire behavior and implementing risk mitigation measures. This thesis proposes a methodological approach using machine learning (ML) algorithms applied to the time series of Copernicus Sentinel-2 satellite data to classify fuel types in Sardinia. The proposed classification models rely on a dataset of spatially distributed parameters characterizing vegetation and land conditions (vegetation index time series, topography, canopy height, and FAPAR) to produce a regional-scale fuel type map with a spatial resolution of 10 meters (Sentinel-2 pixel size). The supervised classification approach is based on a dataset of samples collected through photointerpretation and using a web application developed by some institutes of the National Research Council of Italy (CNR). This study adapted the hierarchical fuel classification system of the European H2020 FirEUrisk project to the specific Mediterranean vegetation and topography of Sardinia. During this thesis work conducted at the CNR, an extensive dataset was collected for training and validating two models: Gradient Tree Boosting (GTB) and Support Vector Machine (SVM), implemented in Python. The implementation required developing Python scripts using Jupyter Notebook, an open-source web application for interactive computing. The classification models achieved promising results in the testing phase, with overall accuracy levels of 79% and 76% for the SVM and GTB models, respectively. These results demonstrate the effectiveness of the models in distinguishing fuel types crucial for creating high-resolution fuel type maps. However, challenges were identified in distinguishing some fuel classes, such as shrubland and grassland. The results of this thesis are of significant interest for the FirEUrisk project and for generating a regional-scale map for one of the project's pilot areas: Sardinia
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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