Search

This review highlights the potential of a political ecology that approaches socionatures more experimentally and speculatively. We first consider theoretical frameworks which can help elucidate a conceptual and methodological pathway for more experimental and speculative political ecology scholarship. Next, we bring together and systematize multiple threads of political ecology scholarship that already lean into experimentation and speculation. We highlight what we see as their potential, particularly focusing on ongoing struggles for climate and environmental justice. First, we conclude that experimental and speculative approaches are critical for laying the foundations of a future-oriented and reparative mode of critique that illuminates emerging possibilities for alternative worlds. Second, these approaches can generate new registers of consciousness that make room for hope, possibility, creativity, and action. Third, experimental and speculative approaches reconfigure political ecological praxis by fostering a more proactive role for the researcher in addressing socioecological challenges as they emerge. It is our hope that this intervention inspires others to do work that is intentionally more experimental or speculative, creating conditions that could potentially lead to a more equitable society in the present with ramifications for a more just future. [ABSTRACT FROM AUTHOR]

Harley Quinn has become an important and popular character in the DC Universe, especially for representations of women scientists in comics and the associated cinematic and TV adaptations. Understandably, much of the analysis of this character has tended to focus on her relationship with the Joker and the gender-based dimensions of this dynamic. However, to distill her identity to these dimensions would be unnecessarily, even unfairly, reductionist, and would narrow our understandings of a rich and complex character. By focussing on her identity as a scientist, this article offers a different analytical lens through which to understand Harley Quinn. Drawing primarily from Stjepan Šejić's Harleen (2020), with supporting insights from appearances in other DC comics, we examine the ways in which her scientific training and aspirations shape her transformation from Harleen Quinzel to Harley Quinn. Specifically, we demonstrate how symmetry, as an orienting concept, is useful for analysing how scientific logics and practices inform and enable this transformation. In doing so, we hope to both enrich understandings of Harley Quinn and foreground science as a potentially useful broader lens through which to understand other significant themes and characters in the DC Universe. [ABSTRACT FROM AUTHOR]

BackgroundControlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. ObjectiveThe purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. MethodsRaw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. ResultsGender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. ConclusionsThe DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.