Impulsivity traits and gambling cognitions associated with gambling preferences and clinical status

Impulsivity (and related traits reward/punishment sensitivity and tolerance to delayed rewards) and gambling cognitions have been linked to gambling. However, their independent associations with gambling preferences and clinical status have never been dissociated. The current study applied a data-driven strategy to identify gambling preferences, based on gambling frequency in several modalities. The two resulting factors were used to classify gambling disorder patients (GDPs) and non-problem recreational gamblers (RGs) into Type I (preferring cards, casino games and skill-based bets) and Type II (preferring slot machines, lotteries/pools and bingo). Participants were assessed in impulsivity, delay discounting, reward/punishment sensitivity, gambling-related cognitions, gambling severity, gambling frequency and average amount gambled per episode. GDPs scored higher than RGs in positive and negative urgency, delay discounting, reward sensitivity and intensity of gambling-related cognitions, but less in lack of perseverance. Additionally, Type II gamblers had greater difficulties delaying gratification, whereas Type I gamblers showed higher cognitive distortion and reward sensitivity levels. In practical terms, the finding that some characteristics are equally pervasive in disordered gamblers independently of their preferences (affect-driven impulsivity), whereas others (distorted cognitions, reward sensitivity, delay discounting) are more prominent in one type or the other, provides a basis to establish targets’ priority in therapy.     

Evaluation of open items using the many-facet Rasch model

The goal of this study is to analyze the quality of ratings assigned to two constructed response questions for evaluating the written ability of essays in Portuguese language from the perspective of the many-facet Rasch (MFR [15 J.M. Linacre, Many-facet Rasch Measurement, 2nd ed., MESA Press, Chicago, 1994. [Google Scholar]]) model. The analyzed data set comes from 350 written tests with two open-item tasks that were developed based on a rating process independently marked by two rater coordinators and a group of 42 raters. The MFR model analysis shows the measurement quality related to the examinees, raters, tasks and items, and classification scale that has been used for the task rating process. The findings indicate significant differences amongst the rater severities and show that the raters cannot be interchanged. The results also suggest that the comparison between the two task difficulties needs further investigation. An additional study has been done on the scale structure of the classification used by each rater for each item. The result suggests that there have been some similarities amongst the tasks and a need of revision for some criteria of the rating process. Overall, the scale of evaluation has shown to be efficient for a classification of the examinees.     

Partnerships as strategic choices in public management

Partnerships are not new phenomena in public administration. However, there is now a more explicit recognition that managing public policy networks involves partnerships within and across policy fields and linking interdependent levels of government nationally and internationally. The aim of this paper is to widen the scope of strategic choice in designing partnerships in public management by developing an alternative approach: the Power-Role Analysis. As the term suggests, Power-Role Analysis uses distinctions between types of power and the roles and relationships corresponding with them to clarify and define different forms of partnership. Power-role analysis provides a basis for considering systematically the problems that arise in managing partnerships of different kinds. It also gives guidance for establishing congruence between forms of partnerships and the results to be expected from selecting one form rather than another. The Power-Role analysis is afterwards performed, its focus being two empirical cases.     

Spatial variability clustering for spatially dependent functional data

This paper introduces a method for clustering spatially dependent functional data. The idea is to consider the contribution of each curve to the spatial variability. Thus, we define a spatial dispersion function associated to each curve and perform a k-means like clustering algorithm. The algorithm is based on the optimization of a fitting criterion between the spatial dispersion functions associated to each curve and the representative of the clusters. The performance of the proposed method is illustrated by an application on real data and a simulation study.     

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981–2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981–2010, 2011–2040, and 2041–2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041–2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.     

Revealing stereotype threat effects and women’s maths performance: the moderating role of mathematical anxiety / Análisis de los efectos de la amenaza del estereotipo y el rendimiento matemático de las mujeres: el rol moderador de la ansiedad matemática

Stereotype threat is a phenomenon by which members of a stereotyped group have their performance impaired when their group identity is made salient. Previous research has found that telling women about stereotype threat prevents impaired performance in mathematical tasks. Moreover, the effect of stereotype threat seems to be moderated by mathematical anxiety. The present work seeks (1) to test the effect of stereotype threat on women’s maths performance taking into account a previous maths score, (2) to replicate in a Spanish sample the protective effect of teaching intervention and (3) to test the moderator role of maths anxiety in the relationship between stereotype threat and impaired performance. Participants (110 undergraduate students) were randomly assigned to either stereotype threat or teaching intervention condition and the decrement in their performance was used as a dependent variable. Stereotype threat only impairs the performance of women with higher maths anxiety scores, and teaching intervention is effective to ameliorate this decrement in performance. We recommend the development of programmes that teach girls about stereotype threat and reduce mathematical anxiety.