12 research outputs found

    Practicing food anxiety: Making Australian mothers responsible for their families’ dietary decisions

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    Concerns about the relationship between diet, weight, and health find widespread expression in the media and are accompanied by significant individual anxiety and responsibilization. However, these pertain especially to mothers, who undertake the bulk of domestic labor involved in managing their families’ health and wellbeing. This article employs the concept of anxiety as social practice to explore the process whereby mothers are made accountable for their families’ dietary decisions. Drawing on data from an Australian study that explored the impact of discourses of childhood obesity prevention on mothers, the article argues that mothers’ engagements with this value-laden discourse are complex and ambiguous, involving varying degrees of self-ascribed responsibility and blame for children's weight and diets. We conclude by drawing attention to the value of viewing food anxiety as social practice, in highlighting issues that are largely invisible in both official discourses and scholarly accounts of childhood obesity prevention

    Vehicle detection and counting from VHR satellite images: efforts and open issues

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    4 pages, planned for a conference submissionDetection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learning-based models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85% with a recall rate also high (76.4% and 71.9% for Tiramisu and YOLO respectively)

    Vulnerability, Relationality, and Dependency: Feminist Conceptual Resources for Food Justice

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    This article articulates how core concepts in feminist ethical and social theory such as vulnerability, relationality, and dependency are central for understanding both injustices in contemporary food systems and how best to pursue food justice. It argues that denials of dependency, relationality, and vulnerability take the form of normal, but ethically problematic, attitudes and practices, such as reductionism, detachment, and privatization, and thus constitute the underlying shared roots of myriad agricultural and food-related injustices. In particular, this feminist approach helps resolve the tension between critiques of the industrial food system and critiques of the sociocultural politics of food and health
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