Travel Profiles Of Family Holidays In Italy

Family represents the most important and emotive connection among humans. In tourism sector, it is the consumer base of the industry; however, the importance of family in travel market is not reflected in tourism research, even if family holiday market has been identified as constituting a major portion of leisure travels around the world. Furthermore, travel choices are clearly influenced by the composition and the characteristics of the families. In this paper, we analyse family holidays in the Italian context; for the purpose of this study, from ISTAT multipurpose survey we use a sample of around 2,000 holidays made in 2013 by almost two components of the same family. The goal is to classify family holidays, and detect their profile

Multi-mode partitioning for text clustering to reduce dimensionality and noises

Co-clustering in text mining has been proposed to partition words and documents simultaneously. Although the main advantage of this approach may improve interpretation of clusters on the data, there are still few proposals on these methods; while one-way partition is even now widely utilized for information retrieval. In contrast to structured information, textual data suffer of high dimensionality and sparse matrices, so it is strictly necessary to pre-process texts for applying clustering techniques. In this paper, we propose a new procedure to reduce high dimensionality of corpora and to remove the noises from the unstructured data. We test two different processes to treat data applying two co-clustering algorithms; based on the results we present the procedure that provides the best interpretation of the data

Effectiveness of Interventions to Reduce Pesticide Exposure in Agriculture

The harmful effects of acute pesticide poisoning have been well documented as an established hazard of agricultural work, while the evidence of the association between chronic pesticide exposure and health consequences, continues to emerge. Despite many pesticides have been banned or restricted in several developed countries, exposures to these toxic agents are still occurring in most of the developing world. The objective of this review is to determine the effectiveness of educational interventions designed to reduce exposure to pesticides in order to prevent health effects in agricultural workers. Intervention approaches to prevent pesticide exposure in agriculture vary vastly from country to country probably depending on the level of development achieved. Although many of the papers on educational safety interventions reported some positive results, the availability of randomized controlled trials for this topic is limited and several interventions exclusively measured changes in attitudes or knowledge of participants, with scarce efforts to determine if there was a consistent reduction in pesticide exposure.We conclude that although educational interventions show some efficacy at raising participants’ awareness of pesticide risks, studies using better quality educational approaches are needed

Introduction to the thematic Session on "Text Analytics in Gender Studies"

In recent years, in all fields of knowledge, a data-driven approach has spread according to the new scenario defined by the Big Data era. The so-called data deluge has started a season where an impressive amount of data constitutes a valuable research material for scholars. In this new context, the data-driven approach enables academics and scientists to examine and organize data with the goal of increasing knowledge in many research areas. The deluge of data today allows us to plan new analyses on a variety of unstructured data that are produced in major part by web navigation. This special issue collects four innovative papers dealing with different problems related to gender studies, but which have as their common thread the applications of text analytics techniques. These studies were discussed during the 14th International Conference on the Statistical Analysis of Textual Data, that was held in Rome (Italy), from 12 to 15 June 2018. This themed section is doubly focused – on both methodology and techniques used to address crucial topics at the core of the discussion on gender

How much has been said about corruption? A text analytics method for literature review

Corruption is a global problem that has a disproportionate impact on the poor and most vulnerable, increasing costs and reducing access to services, including health, education and justice. Italy is ranked at 52nd position on a total of 180 countries surveyed by Transparency International and updated in 2020. Several studies have been conducted worldwide on the phenomenon of corruption from the point of view of many aspects of daily life: political, legal, social, economical, educational, civic and territorial context. The aim of this paper is to explore how the corruption has been threated and the context where it has been investigated in national and international literature by a keyword-string search method of the term ‘corruption’ across academic electronic database (i.e. Scopus), A quantitative coding scheme was implemented to provide descriptive statistics, ranking of the more frequents tokens, co-occurrence analysis, graphs and community detection networks. One of the issues is to study the relationship between words using graphs representation by Fruchterman-Reingold algorithm. This representation helps to visualize the most repeated connections between words along all the literature analyzed. A second issue is to identify communities inside a network using Girvan-Newman algorithm: the subset of nodes that are densely connected to each other and loosely connected to the nodes in the other communities in the same graph leads to capture words or groups of largely used together and brings to identify indicators most used to represent and define the complex and latent phenomenon of corruption. The main contribution of this paper is double: from one side a result is to carry out a new approach to a systematic literature review, “a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps.” (Tricco et al, 2018); strongly linked to the previous one, the second contribution is to discover inside this ‘scoping’ review a set of socio-economic indicators of corruption more discussed and studied on this issue and walk the steps towards the operationalization of such as multidimensional phenomenon

Women active citizenship and wellbeing: the Italian case

The paper analyses several indicators (political participation, trade union organizations, ecological associations, voluntary and cultural activities) of active citizenship in Italy, focusing on the profiles of the activists in relation to wellbeing and gender differences. A sample of around 49,000 individuals and 19,000 families from the survey "Aspect of daily life", collected by the Italian National Institute of Statistics in 2010 was used for the analysis. We apply multivariate statistical methods (correspondence analysis, fuzzy clustering, multidimensional scaling and regression models) to describe the sample and detect the predictors of satisfaction and trust in people

A new composite index for measuring corruption risk at the Italian municipality level

In the previous working paper a set of keywords were identified by finding the most recurrent associated terms that occurred alongside the word ‘corruption’ from a sample pool of academic articles within the Scopus academic database. These recurring keywords or ‘socio-economic indicators’, retrieved through social network analysis techniques (D.F. Iezzi et al. 2020, A.F. Colladon, 2018), are therefore useful to be used as evidence of connections between social factors in studies, debates, and discussions and can be used by policymakers to make good decisions by evaluating specific programs, developing budgets, and setting goals and priorities. The socio-economic indicators (i.e. a value, mostly empirical, with which we want to measure, in a given situation, significant changes in behaviour and social condition) are derived from our research and most of them are placed in the political and economic sphere (sustainable development, underground economy, economic strategies in developing countries, transparency as an anti-corruption mechanism, public awareness) or in the social sphere (education, civilization, amoral familism, nepotism) or technological (digitization process to reduce burdens and improve performance and governance control, dissemination of information). This study aims to utilise these indicators for the purpose of understand ‘corruption’ as a measurable phenomenon after a procedure consising in collecting data from public databases (and open-data datasets) and examining it thereafter by using statistical analysis. The goal of this research is to calculate a composite indicator at the municipal level by collecting data and analysing it to understand how this connection between social factors is distributed in Italy and help to better identify where corruption exists. Three pillars, namely, the dimensions that better represent the concept of the theoretical framework (bad administration, territorial economy, education and civilization) are identified and for each of them, a set of measurable elementary indicators that can be quantified and compared against each other. The choice of each dimension derives from studies, empirical evidence, surveys, and opinions present in the scientific literature on the links between them and corruption. Each pillar becomes a matrix (A. Marradi, 2007) in datasets selected from institutional and certified datasets available on the Internet and combined in a larger matrix following the construction phases of the composite indicator with a non-compensatory approach (Mazziotta, Pareto, 2016). We calculate composite indicators of each pillar and at the end also the composite indicator of all the composite indicators of the pillars. They are calculated, represented, and explained in order to have a unidimensional measure that can help policymakers to understand the complex reality