Time and space as antagonistic forms: the struggle of San Francisco Xochicuautla through the forest of ñatho

En la zona centro de México está en ciernes un agresivo proyecto de reconfiguración y reordenamiento territorial que implica la inversión de capital trasnacional, y que supone la producción de una espacialidad afín a la forma neoextractivista de acumulación. Megaproyectos de infraestructura son parte del paisaje cotidiano, detrás del que está en juego el antagonismo que implica la territorialización del capital neoextractivista. Frente a ello, afirmamos, la defensa del bosque ñatho por parte de la comunidad de Xochicuautla nos permite reactualizar los horizontes de la emancipación, y considerar la lucha de clases en términos de producción de temporalidades y espacialidades. Uno de los aprendizajes posibles de la experiencia de Xochicuautla es el de abordar el antagonismo del capital en términos temporales y espaciales. Esto, afirmamos, nos permite reactualizar los contenidos de la revolución, así como valorar los procesos de lucha desde referentes no afines al pensamiento dominante

Política antiidentitaria y dignidad. El hacer y la revolución desde la vida cotidiana

En los últimos días de 2011 se publicó la versión mexicana de Agrietar el capitalismo de John Holloway. Una vez que inició el 2012 se realizó una serie de presentaciones en espacios académicos como la Universidad Autónoma del Estado de México y el Centro Universitario de Ciencias Sociales de Guadalajara en marzo, así como en la Universidad Nacional Autónoma de México en abril. Este ciclo de presentaciones académicas culminó apenas el 6 de diciembre pasado en la Universidad Benito Juárez de Oaxaca

Learning Web Development using GitHub Copilot in and outside Academia: a Blessing or a Curse?

This article investigates the usage of GitHub Copilot, an artificial intelligence-powered coding assistant owned by Microsoft and GitHub, in the process of learning and teaching web development both in formal academic, and informal settings. We dive into the idea behind utilizing GitHub Copilot and highlight its most common and relevant use cases which can be used to learn Web Development. Drawing from existing scientific literature and online statements from software professionals, we present an overview of the current situation with artificial intelligence-assisted programming tools such as GitHub Copilot and its impact and irrelevance on Web Development education especially for the early learning stages. Professionals both in and outside academia agree that usage of artificial intelligence Pair Programming tools such as GitHub Copilot is neither recommended nor essential when learning or teaching Web Development

Validating Predictions of Unobserved Quantities

The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator