1,720,977 research outputs found
Predicting household resilience with machine learning: preliminary cross-country tests
Full text linkUsing a unique cross-country sample from 10 impact evaluations of development projects, we test the out-of-sample performance of machine learning algorithms in predicting non-resilient households, where resilience is a subjective metrics defined as the perceived ability to recover from shocks. We report preliminary evidence of the potential of these data-driven techniques to identify the main predictors of household resilience and inform the targeting of resilience-oriented policy interventions
Too rare to dare? Leveraging household surveys to boost research on climate migration
No full textNationally representative household surveys are a potential data source that could shed light on the climate–migration nexus. However, they are rarely designed specifically to measure or study migration and often lack the necessary features to identify connections with climate change. This paper offers a critical reflection on current challenges faced by multi-topic household surveys in responding to these needs while also highlighting the many opportunities embedded in their use. Using the Living Standards Measurement Study household survey programme of the World Bank as an example, this paper proposes a methodological agenda and practical guidance to address data gaps and advance research on climate migration
COVID-19 and Local Mortality Estimates
No full textThis chapter provides an overview of the leading research on mortality estimates during the COVID-19 pandemic, with a specific focus on the local level. It first discusses the key data challenges and general methodological issues involving the estimation of the actual number of lives lost to the pandemic at all spatial scales – global, national, and subnational. Next, it reviews the main studies focusing on COVID-19 and local excess mortality for all areas in which reliable figures have been provided, and complements them with an overview of aggregate-level studies for macro-areas and countries in which local estimates currently remain unavailable. The survey highlights the persistence of severe geographic imbalances in data availability and studies coverage, even when focusing on national estimates alone. As it stands, granular excess mortality estimates have been produced only for a tiny and non-representative minority of areas. The study concludes with recommendations about methodological choices and research priorities to reduce existing data and information gaps
Local mortality estimates during the COVID-19 pandemic in Italy
Full text linkEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community
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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