Theory, research, and clinical reports suggest that moral cognitions play a role in initiating and sustaining criminal behavior. The 25 item Criminogenic Cognitions Scale (CCS) was designed to tap 5 dimensions: Notions of entitlement; Failure to Accept Responsibility; Short-Term Orientation; Insensitivity to Impact of Crime; and Negative Attitudes Toward Authority. Results from 552 jail inmates support the reliability, validity, and predictive utility of the measure. The CCS was linked to criminal justice system involvement, self-report measures of aggression, impulsivity, and lack of empathy. Additionally, the CCS was associated with violent criminal history, antisocial personality, and clinicians’ ratings of risk for future violence and psychopathy (PCL:SV). Furthermore, criminogenic thinking upon incarceration predicted subsequent official reports of inmate misconduct during incarceration. CCS scores varied somewhat by gender and race. Research and applied uses of CCS are discussed.

Due to rising online competition, increasing cost pressure and cross-channel customer journeys, stationary retail has tried to develop innovative value propositions and co-create value with customers through new technologies, which are expected to profoundly change the stationary retail’s service systems. Among other technologies, service robots are said to have the potential to revitalise interactive value creation in stationary retail. However, the integration of such technologies poses new challenges. Prior research has looked at customers’ acceptance of service robots in stationary retail settings, but few studies have explored their counterparts – the frontline employees’ (FLEs) perspective. Yet, FLEs’ acceptance of service robots is crucial to implement service robots for retail innovation. To explore FLEs’ acceptance of and resistance to service robots, a qualitative exploratory interview study is conducted. It identifies decisive aspects, amongst others loss of status or role incongruency. The findings extend prior studies on technology acceptance and resistance and reveal i.a. that FLEs perceive service robots as both a threat and potential support. Moreover, they feel hardly involved in the co-creation of use cases for a service robot, although they are willing to contribute.

Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

BackgroundThe aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed the model's performance for this application and compared it to previous models used for this task.MethodsUsing 108 expert-annotated clinical trial abstracts and 2,000 unlabeled abstracts this study develops a three-class sentiment classification algorithm for clinical trial abstracts. The model uses a semi-supervised model based on the Bidirectional Encoder Representation from Transformers (BERT) model, a much more advanced and accurate method compared to previously used models based upon traditional machine learning methods. The prediction performance was compared to those previous studies.ResultsThe algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, significantly outperforming previous studies used to classify sentiment in clinical trial literature, while also making the sentiment classification finer grained with greater reproducibility.ConclusionWe demonstrate an easily applied sentiment classification model for clinical trial abstracts that significantly outperforms previous models with greater reproducibility and applicability to large-scale study of reporting trends.

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