Salience games : private politics when public attention is limited

We develop a theoretical model in which an industry and NGO play salience games - they act strategically to influence public attention to social impacts in the sector. Salience stimulates extra donations for the NGO, and thus firms have incentives to hide the damage they do to avoid public attention. How can an NGO design its mission (how to divide income between campaigning and other projects, and what sorts of campaigns to run) to thrive in such a setting? We show that when public attention is scarce, a greater campaign orientation induces industry to invest in greater obfuscation, starving the NGO of funds. The NGO in turn strategically biases its mission away from campaigns, and in favor of sector-wide versus firm-specific campaigns, but not by as much as a welfare-motivated planner would want. When public attention is avoided by a mixture of substantive and symbolic action, we show that a greater weight on the former induces the NGO to become more campaign-oriented, with social damage lower

Towards Interactive Multidimensional Visualisations for Corpus Linguistics

We propose the novel application of dynamic and interactive visualisation techniques to support the iterative and exploratory investigations typical of the corpus linguistics methodology. Very large scale text analysis is already carried out in corpus-based language analysis by employing methods such as frequency profiling, keywords, concordancing, collocations and n-grams. However, at present only basic visualisation methods are utilised. In this paper, we describe case studies of multiple types of key word clouds, explorer tools for collocation networks, and compare network and language distance visualisations for online social networks. These are shown to fit better with the iterative data-driven corpus methodology, and permit some level of scalability to cope with ever increasing corpus size and complexity. In addition, they will allow corpus linguistic methods to be used more widely in the digital humanities and social sciences since the learning curve with visualisations is shallower for non-expert

Basic and complex emotion recognition in children with autism: cross-cultural findings.

BACKGROUND: Children with autism spectrum conditions (ASC) have emotion recognition deficits when tested in different expression modalities (face, voice, body). However, these findings usually focus on basic emotions, using one or two expression modalities. In addition, cultural similarities and differences in emotion recognition patterns in children with ASC have not been explored before. The current study examined the similarities and differences in the recognition of basic and complex emotions by children with ASC and typically developing (TD) controls across three cultures: Israel, Britain, and Sweden. METHODS: Fifty-five children with high-functioning ASC, aged 5-9, were compared to 58 TD children. On each site, groups were matched on age, sex, and IQ. Children were tested using four tasks, examining recognition of basic and complex emotions from voice recordings, videos of facial and bodily expressions, and emotional video scenarios including all modalities in context. RESULTS: Compared to their TD peers, children with ASC showed emotion recognition deficits in both basic and complex emotions on all three modalities and their integration in context. Complex emotions were harder to recognize, compared to basic emotions for the entire sample. Cross-cultural agreement was found for all major findings, with minor deviations on the face and body tasks. CONCLUSIONS: Our findings highlight the multimodal nature of ER deficits in ASC, which exist for basic as well as complex emotions and are relatively stable cross-culturally. Cross-cultural research has the potential to reveal both autism-specific universal deficits and the role that specific cultures play in the way empathy operates in different countries.The research leading to this work has received funding from the European Community’s Seventh Framework Programme (FP7) under grant agreement no. [289021]. SvB was supported by the Swedish Research Council (Grant No. 523-2009-7054), and SBC was supported by the Autism Research Trust, the MRC, the Wellcome Trust, and the NIHR CLAHRC EoE during the period of this work

How customers ‘learn’ to work for retailers

The purpose of this paper is to investigate how learning style affects the performance of the ‘working’ customer in one self-service context – retail Self Check-Out Tills (SCOT). For the purposes of this paper, we have adopted the UK term Self Check-Out Tills (SCOT), also known as ‘self-service registers’ in other countries, to describe this form of self-service in retail stores). The study uses qualitative and quantitative data collected from users of retail SCOT. Initial exploratory factor analysis of 232 SCOT users revealed significant differences in learning styles. Three categories emerged: ‘regular reassurance’, ‘motivated practice’ and ‘cautious discovery’. Customers adopting different learning styles varied in their perceptions of ability and enjoyment with SCOT, and in their capability of helping other customers with SCOT. The demographic make-up of customers adopting the different learning styles was also shown to vary. Previously, little has been done to identify the specific training needs of working customers. This research begins to address this knowledge gap

A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation

Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing NLP tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12% to 91.08% on individual test texts. Applying time-sensitive methods improved results by as much as 3.54% and by 1.72% on average

Livestock-associated methicillin and multidrug resistant Staphylococcus aureus is present among industrial, not antibiotic-free livestock operation workers in North Carolina

Objectives Administration of antibiotics to food animals may select for drug-resistant pathogens of clinical significance, such as methicillin-resistant Staphylococcus aureus (MRSA). In the United States, studies have examined prevalence of MRSA carriage among individuals exposed to livestock, but prevalence of multidrug-resistant S. aureus (MDRSA) carriage and the association with livestock raised with versus without antibiotic selective pressure remains unclear. We aimed to examine prevalence, antibiotic susceptibility, and molecular characteristics of S. aureus among industrial livestock operation (ILO) and antibiotic-free livestock operation (AFLO) workers and household members in North Carolina. Methods Participants in this cross-sectional study were interviewed and provided a nasal swab for S. aureus analysis. Resulting S. aureus isolates were assessed for antibiotic susceptibility, multi-locus sequence type, and absence of the scn gene (a marker of livestock association). Results Among 99 ILO and 105 AFLO participants, S. aureus nasal carriage prevalence was 41% and 40%, respectively. Among ILO and AFLO S. aureus carriers, MRSA was detected in 7% (3/41) and 7% (3/42), respectively. Thirty seven percent of 41 ILO versus 19% of 42 AFLO S. aureus-positive participants carried MDRSA. S. aureus clonal complex (CC) 398 was observed only among workers and predominated among ILO (13/34) compared with AFLO (1/35) S. aureus-positive workers. Only ILO workers carried scn-negative MRSA CC398 (2/34) and scn-negative MDRSA CC398 (6/34), and all of these isolates were tetracycline resistant. Conclusions Despite similar S. aureus and MRSA prevalence among ILO and AFLO-exposed individuals, livestock-associated MRSA and MDRSA (tetracycline-resistant, CC398, scn-negative) were only present among ILO-exposed individuals. These findings support growing concern about antibiotics use and confinement in livestock production, raising questions about the potential for occupational exposure to an opportunistic and drug-resistant pathogen, which in other settings including hospitals and the community is of broad public health importance

Source tracking swine fecal waste in surface water proximal to swine concentrated animal feeding operations

Swine farming has gone through many changes in the last few decades, resulting in operations with a high animal density known as confined animal feeding operations (CAFOs). These operations produce a large quantity of fecal waste whose environmental impacts are not well understood. The purpose of this study was to investigate microbial water quality in surface waters proximal to swine CAFOs including microbial source tracking of fecal microbes specific to swine. For one year, surface water samples at up- and downstream sites proximal to swine CAFO lagoon waste land application sites were tested for fecal indicator bacteria (fecal coliforms, Escherichia coli and Enterococcus) and candidate swine-specific microbial source-tracking (MST) markers (Bacteroidales Pig-1-Bac, Pig-2-Bac, and Pig-Bac-2, and methanogen P23-2). Testing of 187 samples showed high fecal indicator bacteria concentrations at both up- and downstream sites. Overall, 40%, 23%, and 61% of samples exceeded state and federal recreational water quality guidelines for fecal coliforms, E. coli, and Enterococcus, respectively. Pig-1-Bac and Pig-2-Bac showed the highest specificity to swine fecal wastes and were 2.47 (95% confidence interval [CI] = 1.03, 5.94) and 2.30 times (95% CI = 0.90, 5.88) as prevalent proximal down- than proximal upstream of swine CAFOs, respectively. Pig-1-Bac and Pig-2-Bac were also 2.87 (95% CI = 1.21, 6.80) and 3.36 (95% CI = 1.34, 8.41) times as prevalent when 48 hour antecedent rainfall was greater than versus less than the mean, respectively. Results suggest diffuse and overall poor sanitary quality of surface waters where swine CAFO density is high. Pig-1-Bac and Pig-2-Bac are useful for tracking off-site conveyance of swine fecal wastes into surface waters proximal to and downstream of swine CAFOs and during rain events