The trade-offs between economic efficiency and job creation in olive grove smallholdings

Cooperation among farmers is an effective alternative to reduce production costs in smallholdings of traditional olive groves and increase their low profitability. However, an increase in efficiency reduces working hours and the labour required for the management of farms. This fact can lead to negative effects in rural areas, especially considering the importance of the social aspect of the crop. Therefore, to contribute to the global discussion on the transition towards more sustainable farming, it is important to know, ex ante, what level of employment is generated by olive groves and what could be lost under more competitive and efficient scenarios. The purpose of the work is to quantify the expected impact on employment by the implementation of cooperative management methods in order to generate true and accurate information that can be used in the design of future policies that aim to increase the economic profitability of small olive farms. The results make clear that this way of managing farms, by increasing the efficiency of agricultural work, has a significant negative impact on employment. However, this management formula does encourage the professionalisation of the sector by generating quality employment, in addition to contributing to the preservation of small farms and the socio-cultural and territorial benefits they generate.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Managers’ exploration activities and individual unlearning: the mediating role of learning orientation and reflection

Abstract

The main goal of this study was to investigate the effects of managers’ exploration activities on learning orientation, reflection, and unlearning of team members. A questionnaire survey was conducted among 115 employees in 23 teams from a Japanese pharmaceutical company. The results of multi-level analyses suggest that managers’ exploration activities had a direct influence on team members’ learning orientations, which subsequently had a positive influence on their unlearning, with and without the mediation of reflection. These findings suggest that a subordinate’s unlearning is driven by managers’ activities through motivational and cognitive processes. The theoretical and practical implications of the findings are discussed.     

Workplace civility training: understanding drivers of motivation to learn

Abstract

Training is recommended as an important human resource management (HRM) practice to prevent mistreatment and enhance civility, but little is known about what influences the effectiveness of civility training. The central aim of this study was to address how workgroup conditions influence employees' attitudes about civility training and motivation to learn, which previous research shows is a predictor of training outcomes. Predictors were posited to include psychological and workgroup climate for civility, and personal and ambient mistreatment experiences. These predictors were hypothesized to drive positive (training discrepancy) and negative (training skepticism) pre‐training attitudes, which in turn were expected to influence motivation to learn. Results suggest the influence of climate for civility and mistreatment experiences on motivation to learn is largely indirect via pre‐training attitudes. Training skepticism and training discrepancy have conflicting influences on motivation to learn. Findings provide an empirical basis for HRM professionals to maximize employee motivation to learn in their own civility interventions.     

互文性理论视角下大学英语写作教学方法探析

互文性理论认为每个语篇都是对其他语篇的吸收和转化，语篇不是孤立的，总是存在于语篇网络中。互文性是西方结构主义和后现代主义文化思潮中产生的文本理论，但是目前对于互文性理论的研究多以文学批评理论的研究和文学作品研究为主，作为教学策略应用于英语教学的研究则为数不多。本文通过探讨互文性理论指导下大学英语写作教学的实践意义及目前大学英语写作教学存在的问题，提出了笔者在教学实践中所积累的几点写作教学法，以期为大学英语写作教学模式提供新的思路。     

构建高校大学生党员教育新模式探索

为进一步提升高校党建实效,高校大学生党员教育工作亟待创新型机制,笔者以高校志愿工作为突破口,通过分析志愿工作之现状,探索其和学生党员教育工作的联动机制,旨在为当前学生党员教育工作注入新的生机和活力。     

中高职三二分段下的学生素质拓展系统的设计与实现

针对高职教育发展的新形势、新政策、新要求，中高职三二分段教育成为高职教育的一部分，传统大学生素质拓展系统均适用于普通高校，设计开发一个适用于高职教育，针对中高职学生特点、职业教育培养目标的学生素质拓展系统非常有必要，有利于职业技能人才的全面培养，也减轻了学校相关工作人员的工作量，为师生带来方便。