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Mutual benefit societies evolved as the major provider for sickness, accident and life insurance in the late nineteenth and early twentieth centuries on both sides of the Atlantic. One of the major problems facing insurers was the risk of adverse selection, i.e. that unhealthy individuals had more incentives than healthy individuals to insure when priced for the average risk. By empirically examining whether longevity among insured individuals in a nationwide mutual health society was different from a matched sample of uninsured individuals, we seek to identify the presence of adverse selection. We find no compelling evidence showing that unhealthy individuals were more likely to insure, or reasons to believe that problems related to adverse selection would have been a major reason for government intervention in the health insurance market in Sweden.

Research using traditional experimental paradigms (e.g., Priming, Stroop and Simon tasks), narratives and interview type data have revealed that bilingual speakers process and express emotion differently in their two languages. In the current study, both a qualitative and quantitative approach were taken to investigate how individuals who know and regularly use several languages process emotion in each of their languages. In Experiment 1, emotional stimuli in the L2 and L3 was quantitatively investigated using an Affective Simon Task. Participants consisted of Sub-Saharan African multilinguals who had acquired Kiswahili (L2) after their mother tongue (L1), followed by English (L3). The results revealed no difference in the way emotion and emotion-laden words were processed in the two languages (Kiswahili and English). However, significant Affective Simon Effects emerged for positive emotion and emotion-laden words, suggesting that these multilinguals largely process positive emotions in their L2 and L3. In Experiment 2, narrative data generated by multilinguals was used to determine how language selection was influenced by context and type of emotional situation. Themes that emerged within the qualitative analysis revealed that one’s L1 was the more emotional language when expressing negative emotions, while the L2 and L3 were reported to be used more frequently when expressing positive emotions, or when discussing more sensitive or embarrassing topics.

This study characterized breeding, housing, feeding and health management practices in positive deviants and typical average performing smallholder dairy farms in Tanzania. The objective was to distinguish management practices that positive deviant farms deploy differently from typical farms to ameliorate local prevalent environmental stresses. In a sample of 794 farms, positive deviants were classified on criteria of consistently outperforming typical farms (p < 0.05) in five production performance indicators: energy balance ≥ 0.35 Mcal NEL/d; disease-incidence density ≤ 12.75 per 100 animal-years at risk; daily milk yield ≥ 6.32 L/cow/day; age at first calving ≤ 1153.28 days; and calving interval ≤ 633.68 days. The study was a two-factor nested research design, with farms nested within the production environment, classified into low- and high-stress. Compared to typical farms, positive deviant farms had larger landholdings, as well as larger herds comprising more high-grade cattle housed in better quality zero-grazing stall units with larger floor spacing per animal. Positive deviants spent more on purchased fodder and water, and sourced professional veterinary services (p < 0.001) more frequently. These results show that management practices distinguishing positive deviants from typical farms were cattle upgrading, provision of larger animal floor spacing and investing more in cattle housing, fodder, watering, and professional veterinary services. These distinguishing practices can be associated with amelioration of feed scarcity, heat load stresses, and disease infections, as well as better animal welfare in positive deviant farms. Nutritional quality of the diet was not analyzed, for which research is recommended to ascertain whether the investments made by positive deviants are in quality of feeds.

In smallholder dairy-cattle farming, identifying positive deviants that attain outstanding performance can inform targeted improvements in typical, comparable farms under similar environmental stresses. Mostly, positive deviants are identified subjectively, introducing bias and limiting generalisation. The aim of the study was to objectively identify positive deviant farms using the Pareto-optimality ranking technique in a sample of smallholder dairy farms under contrasting stressful environments in Tanzania to test the hypothesis that positive deviant farms that simultaneously outperform typical farms in multiple performance indicators also outperform in yield gap, productivity and livelihood benefits. The selection criteria set five performance indicators: energy balance ≥ 0.35 Mcal NEL/d, disease-incidence density ≤ 12.75 per 100 animal-years at risk, daily milk yield ≥ 6.32 L/cow/day, age at first calving ≤ 1153.28 days and calving interval ≤ 633.68 days. Findings proved the hypothesis. A few farms (27: 3.4%) emerged as positive deviants, outperforming typical farms in yield gap, productivity and livelihood benefits. The estimated yield gap in typical farms was 76.88% under low-stress environments and 48.04% under high-stress environments. On average, total cash income, gross margins and total benefits in dairy farming were higher in positive deviants than in typical farms in both low- and high-stress environments. These results show that the Pareto-optimality ranking technique applied in a large population objectively identified a few positive deviant farms that attained higher productivity and livelihood benefits in both low- and high-stress environments. However, positive deviants invested more in inputs. With positive deviant farms objectively identified, it is possible to characterise management practices that they deploy differently from typical farms and learn lessons to inform the uptake of best practices and extension messages to be directed to improving dairy management.

Sentiment analysis is a method to identify people’s attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.

Abstract On the surface, Islam in Kenya presents a continued competition between Sufi-oriented orders and the Salafi-minded Muslims. This article argues that the controversies between “traditional” and “modern” forms of Islam in the country is an indication of the plurality of ways in which Muslims, in a minority context, makes sense of their religious identities. Changing political circumstances in Kenya in the colonial and the post-colonial eras changed the conditions of Muslims in Kenya, thereby explaining their present attitude towards the state. Since the commencement of the war on terror in 2001, Kenyan Muslims have been confronted with rising radicalization amongst certain members of the community and increasing cases of kidnapping and extra judicial killing targeting members of the Muslim population. The result is a complex and problematic relationship with the state, characterised by Muslims’ continuous demands for justice and equal treatment as citizens by the state.