Constructing a climate change logic: An institutional perspective on the "tragedy of the commons"

Despite increasing interest in transnational fields, transnational commons have received little attention. In contrast to economic models of commons, which argue that commons occur naturally and are prone to collective inaction and tragedy, we introduce a social constructionist account of commons. Specifically, we show that actor-level frame changes can eventually lead to the emergence of an overarching, hybrid "commons logic" at the field level. These frame shifts enable actors with different logics to reach a working consensus and avoid "tragedies of the commons." Using a longitudinal analysis of key actors' logics and frames, we tracked the evolution of the global climate change field over 40 years. We bracketed time periods demarcated by key field-configuring events, documented the different frame shifts in each time period, and identified five mechanisms (collective theorizing, issue linkage, active learning, legitimacy seeking, and catalytic amplification) that underpin how and why actors changed their frames at various points in time-enabling them to move toward greater consensus around a transnational commons logic. In conclusion, the emergence of a commons logic in a transnational field is a nonlinear process and involves satisfying three conditions: (1) key actors view their fates as being interconnected with respect to a problem issue, (2) these actors perceive their own behavior as contributing to the problem, and (3) they take collective action to address the problem. Our findings provide insights for multinational companies, nation-states, nongovernmental organizations, and other stakeholders in both conventional and unconventional commons

Context inference for mobile applications in the UPCASE project, in

Abstract. The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios. Key words: Context-aware services, context inference, smartphones, wearable sensors, decision trees

Smartphone-based monitoring of Parkinson disease:quasi-experimental study to quantify hand tremor severity and medication effectiveness

Abstract Background: Hand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Objective: Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Methods: Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. Results: We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Conclusions: Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom