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When ‘Can I help you?’ hurts:Roma experiences of everyday microaggressions in retail outlets
No full textThe concept of microaggressions alerts us how majority group members' everyday behaviour can impact minorities negatively. Recently, some researchers have questioned the criteria for identifying microaggressions and rejected the concept's utility. We maintain that attending to minorities' everyday experiences is important and illustrate this through a three-phase study with Roma in Hungary. First, we conducted interviews exploring their everyday interactional experiences (Phase 1, N = 17); second, Roma participants filmed (naturally occurring) interactions with majority group members (Phase 2, N = 10); third, we showed such filmed interactions to Roma focus groups and recorded their discussions (Phase 3, N = 28). Analysing these discussions, we focused on how the experience of surveillance when shopping (even when manifested in apparently helpful attention from shop assistants) impacted participants in ways that majority group members likely have little awareness of. Specifically, participants reported their need to (a) reflect on (and manage) their emotional reactions; (b) weigh a variety of strategic considerations as to how to respond; and (c) engage in in-the-moment interpretation as to the nature of the interaction. Such experiences negatively impact the use of public space and illustrate the value of adopting the minority's vantage point concerning the identification of microaggressive treatment
Network Reconstruction via the Minimum Description Length Principle
No full textA fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting and produces an inferred network with a statistically justifiable number of edges and their weight distribution. The status quo in this context is based on L1 regularization combined with cross-validation. However, besides its high computational cost, this commonplace approach unnecessarily ties the promotion of sparsity, i.e., abundance of zero weights, with weight "shrinkage."This combination forces a trade-off between the bias introduced by shrinkage and the network sparsity, which often results in substantial overfitting even after cross-validation. In this work, we propose an alternative nonparametric regularization scheme based on hierarchical Bayesian inference and weight quantization, which does not rely on weight shrinkage to promote sparsity. Our approach follows the minimum description length principle, and uncovers the weight distribution that allows for the most compression of the data, thus avoiding overfitting without requiring cross-validation. The latter property renders our approach substantially faster and simpler to employ, as it requires a single fit to the complete data, instead of many fits for multiple data splits and choice of regularization parameter. As a result, we have a principled and efficient inference scheme that can be used with a large variety of generative models, without requiring the number of reconstructed edges and their weight distribution to be known in advance. In a series of examples, we also demonstrate that our scheme yields systematically increased accuracy in the reconstruction of both artificial and empirical networks. We highlight the use of our method with the reconstruction of interaction networks between microbial communities from large-scale abundance samples involving on the order of 104-105 species and demonstrate how the inferred model can be used to predict the outcome of potential interventions and tipping points in the system
