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This study investigates the contemporary grammaticalized uses of perdón (‘sorry’) in two varieties of Spanish, namely Mexican and Peninsular Spanish. Methodologically, the investigation is based on a taxonomy of offenses, organized around the concept of ‘face’ and based on spoken data of Spanish from Mexico and Spain. This taxonomy turns out to be a fruitful methodological tool for the analysis of apologetic markers: it does not only offer usage-based evidence for previous theorizing concerning the grammaticalization process of apologetic markers, but also leads to a refinement of these previous results from a contrastive point of view. Evidence from both corpora suggests a more advanced stage in the grammaticalization process of perdón in Mexican Spanish, where it can be used not only as a self-face-saving device geared towards the positive face of the speaker, but also in turn-taking contexts oriented towards the negative face of the interlocutor. Peninsular Spanish, on the other hand, resorts to a more varied gamut of apologetic markers in these contexts.

El artículo analiza a los municipios que conforman al estado de Quintana Roo, México, durante el periodo 2020-2022, con el objetivo de indagar la relación entre la vulnerabilidad municipal y los efectos de la COVID-19. Utiliza una metodología cuantitativa a través del diseño de los índices de vulnerabilidad municipal y de los efectos de la COVID-19, ambos estructurados con información documentada de instituciones gubernamentales. Se propone como hipótesis que, a menor vulnerabilidad municipal, los efectos de la COVID-19 disminuyen. Para su comprobación se aplican métodos de estadística descriptiva y de regresión, y de distancia euclidiana. Los resultados muestran la asociación propuesta entre las variables analizadas; esto quiere decir que, entre menor es la vulnerabilidad municipal los efectos de la COVID-19 serán menores en el conjunto de los municipios. Esta información resulta importante en la toma de decisiones y el diseño de políticas públicas en la etapa pos- COVID-19 en el estado de Quintana Roo.

AbstractThis article traces trends of income inequality in Mexico City from 1770 to 1930 by measuring the gaps between urban real estate rents and unskilled wages. The article presents the first long‐term series of real estate values and rental income for Mexico. One series summarizes the price of an apartment in tenement housing (the prevalent type of popular housing in Mexico), while the other relies on newspaper ads, notarial records, and other sources to estimate property values and rental yield (rental revenue relative to property values). From these wage and rental income series, we calculate rental–wage ratios that are broadly representative of the income gaps between the wealthy and unskilled workers. We find that, at the end of the eighteenth century, inequality moderately increased, followed by a more egalitarian period in the first half of the nineteenth century, and a ballooning in the last quarter of the nineteenth century that persisted into 1930. While inequality receded after the insurrection in the 1810s, it remained high after the Mexican Revolution. We hypothesize that inequality was sensitive to economic growth, and that generalized violence did not universally temper inequality.

The rapid evolution of the Internet of Everything (IoE) has significantly enhanced global connectivity and multimedia content sharing, simultaneously escalating the unauthorized distribution of multimedia content, posing risks to intellectual property rights. In 2022 alone, about 130 billion accesses to potentially non-compliant websites were recorded, underscoring the challenges for industries reliant on copyright-protected assets. Amidst prevailing uncertainties and the need for technical and AI-integrated solutions, this study introduces two pivotal contributions. First, it establishes a novel taxonomy aimed at safeguarding and identifying IoE-based content infringements. Second, it proposes an innovative architecture combining IoE components with automated sensors to compile a dataset reflective of potential copyright breaches. This dataset is analyzed using a Bidirectional Encoder Representations from Transformers-based advanced Natural Language Processing (NLP) algorithm, further fine-tuned by a dense neural network (DNN), achieving a remarkable 98.71% accuracy in pinpointing websites that violate copyright.