Correction: Li et al. Quantitative Evaluation of China’s Pork Industry Policy: A PMC Index Model Approach. Agriculture 2021, 11, 86

The authors found some omissions and errors in the original paper [...]

MIA

Since online discussion platforms can limit the perception of social cues, effective collaboration over videochat requires additional attention to conversational skills. However, self-affirmation and defensive bias theories indicate that feedback may appear confrontational, especially when users are not motivated to incorporate them. We develop a feedback chatbot that employs Motivational Interviewing (MI), a directive counseling method that encourages commitment to behavior change, with the end goal of improving the user's conversational skills. We conduct a within-subject study with 21 participants in 8 teams to evaluate our MI-agent 'MIA' and a non-MI-agent 'Roboto'. After interacting with an agent, participants are tasked with conversing over videochat to evaluate candidate résumés for a job circular. Our quantitative evaluation shows that the MI-agent effectively motivates users, improves their conversational skills, and is likable. Through a qualitative lens, we present the strategies and the cautions needed to fulfill individual and team goals during group discussions. Our findings reveal the potential of the MI technique to improve collaboration and provide examples of conversational tactics important for optimal discussion outcomes.

Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.