Gilmária Salviano Ramos, Silvia Maria Fávero Arend
O feminicídio é crime previsto pelo Código Penal Brasileiro (BRASIL, 1940), inciso VI, §§ 2º e 2º-A, o qual alterou o Art. 121. Foi incluído na legislação brasileira pela Lei nº 13.104, de 2015, com pena de 12 a 30 anos de prisão, agravada de um terço se a vítima estiver grávida ou se três meses após o parto. O presente artigo pretende descrever e analisar os discursos de operadores da justiça como base em inquéritos policiais e boletins de ocorrência sobre casos de feminicídios e tentativas de feminicídio, registrados pela Delegacia de Proteção à Criança, Adolescente, Mulher e Idoso, de São José (DPCAMI), em Santa Catarina, entre o período de 2012 a 2020. Para tanto, as discussões acerca dos movimentos feministas, legislações voltadas para a violência contra mulheres à luz do conceito de interseccionalidade foram fundamentais para a investigação. No que diz respeito a relação entre vítimas e acusados, as mortes de mulheres em Santa Catarina ocorreram no contexto de relacionamentos íntimos e/ou afetivos. É o que denomina-se “Feminicídio direto”: mortes por agressão física, mortes envolvendo violência sexual, mortes envolvendo violência conjugal, doméstica ou familiar, mortes que envolvam tortura psicológica ou violência que incida na degradação do corpo físico da mulher.
In this paper, we study involutive non-degenerate set-theoretic solutions of the Yang-Baxter equation with regular displacement group. In particular, we completely describe the blocks of imprimitivity and the congruences of the irretractable ones, that we show belonging to the class of the latin solutions. Among these solutions, we characterise the simple ones having nilpotent permutation group. A more precise description involving the First Weyl Algebra will be provided when the displacement group is abelian and normal in the total permutation group, and we enumerate and classify the simple ones having minimal size $p^p$, for an arbitrary prime number $p$. Finally, we illustrate our results by some examples.
Alba María Mármol-Romero, Manuel García-Vega, Miguel Ángel García-Cumbreras
et al.
This paper presents a chatbot-based system to engage young Spanish people in the awareness of certain mental disorders through a self-disclosure technique. The study was carried out in a population of teenagers aged between 12 and 18 years. The dialogue engine mixes closed and open conversations, so certain controlled messages are sent to focus the chat on a specific disorder, which will change over time. Once a set of trial questions is answered, the system can initiate the conversation on the disorder under the focus according to the user's sensibility to that disorder, in an attempt to establish a more empathetic communication. Then, an open conversation based on the GPT-3 language model is initiated, allowing the user to express themselves with more freedom. The results show that these systems are of interest to young people and could help them become aware of certain mental disorders.
The diffusion of artificial intelligence, particularly generative models, is expected to transform labor markets in uneven ways across sectors, territories, and social groups. This paper proposes a methodological framework to estimate the potential exposure of employment to AI using sector based data, addressing the limitations of occupation centered approaches in the Spanish context. By constructing an AI CNAE incidence matrix and applying it to provincial employment data for the period 2021 to 2023, we provide a territorial and gender disaggregated assessment of AI exposure across Spain. The results reveal stable structural patterns, with higher exposure in metropolitan and service oriented regions and a consistent gender gap, as female employment exhibits higher exposure in all territories. Rather than predicting job displacement, the framework offers a structural perspective on where AI is most likely to reshape work and skill demands, supporting evidence based policy and strategic planning.
Shuxiang Du, Ana Guerberof Arenas, Antonio Toral
et al.
This study examines the variability of Chat-GPT machine translation (MT) outputs across six different configurations in four languages,with a focus on creativity in a literary text. We evaluate GPT translations in different text granularity levels, temperature settings and prompting strategies with a Creativity Score formula. We found that prompting ChatGPT with a minimal instruction yields the best creative translations, with "Translate the following text into [TG] creatively" at the temperature of 1.0 outperforming other configurations and DeepL in Spanish, Dutch, and Chinese. Nonetheless, ChatGPT consistently underperforms compared to human translation (HT).
Hate speech detection deals with many language variants, slang, slurs, expression modalities, and cultural nuances. This outlines the importance of working with specific corpora, when addressing hate speech within the scope of Natural Language Processing, recently revolutionized by the irruption of Large Language Models. This work presents a brief analysis of the performance of large language models in the detection of Hate Speech for Rioplatense Spanish. We performed classification experiments leveraging chain-of-thought reasoning with ChatGPT 3.5, Mixtral, and Aya, comparing their results with those of a state-of-the-art BERT classifier. These experiments outline that, even if large language models show a lower precision compared to the fine-tuned BERT classifier and, in some cases, they find hard-to-get slurs or colloquialisms, they still are sensitive to highly nuanced cases (particularly, homophobic/transphobic hate speech). We make our code and models publicly available for future research.
This article summarizes some mostly unsuccessful attempts to understand authorial style by examining the attention of various neural networks (LSTMs and CNNs) trained on a corpus of classical Latin verse that has been encoded to include sonic and metrical features. Carefully configured neural networks are shown to be extremely strong authorship classifiers, so it is hoped that they might therefore teach `traditional' readers something about how the authors differ in style. Sadly their reasoning is, so far, inscrutable. While the overall goal has not yet been reached, this work reports some useful findings in terms of effective ways to encode and embed verse, the relative strengths and weaknesses of the neural network families, and useful (and not so useful) techniques for designing and inspecting NN models in this domain. This article suggests that, for poetry, CNNs are better choices than LSTMs -- they train more quickly, have equivalent accuracy, and (potentially) offer better interpretability. Based on a great deal of experimentation, it also suggests that simple, trainable embeddings are more effective than domain-specific schemes, and stresses the importance of techniques to reduce overfitting, like dropout and batch normalization.
Francisco J. Ribadas-Pena, Shuyuan Cao, Elmurod Kuriyozov
In this paper, we describe our participation in the MESINESP Task of the BioASQ biomedical semantic indexing challenge. The participating system follows an approach based solely on conventional information retrieval tools. We have evaluated various alternatives for extracting index terms from IBECS/LILACS documents in order to be stored in an Apache Lucene index. Those indexed representations are queried using the contents of the article to be annotated and a ranked list of candidate labels is created from the retrieved documents. We also have evaluated a sort of limited Label Powerset approach which creates meta-labels joining pairs of DeCS labels with high co-occurrence scores, and an alternative method based on label profile matching. Results obtained in official runs seem to confirm the suitability of this approach for languages like Spanish.
English Abstract:
Corazón de Maíz gathers the stories and recipes for traditional Maya cooking in the regions of Huehuetenango, and is designed to promote the survival of the culinary system of the grand-mothers, and the food that they prepared with love for the whole family. This “culinary novel” contains myths, legends, stories of the origin of some foods, cooking tech-niques and methods. The foods prepared in the altiplano region developed from millenary traditions, the Hispanic invasion, the ravages of climate change bringing droughts and plagues that destroy life, and the various consequences and influences of migration. For the Maya, corn has always been the main and most sacred food. The stories and recipes given in this factual and artist's view of Q'anjob'al Maya food have continuing connections to the worldview embodied in the Popol Wuj, which states that everything that exists on earth has life, if it has life it has a spirit and if it has a spirit then it is sacred, thus the grandparents say that corn is a sacred food that has its own spirit and energy.
Document is in Spanish.
Resumen español:
Corazón de Maíz está centrado en el arte culinario maya y está diseñado para promover la supervivencia del arte y sistema culinario de las abuelas en las diferentes regiones de Huehuetenango, comida que ellas preparaban con amor para toda la familia. Esta «novela culinaria» contiene mitos, leyendas, relatos del origen de algunos alimentos, técnicas y métodos de cocción. Los alimentos que se preparan en la región del alti-plano se desarrollaron a partir de las tradiciones milenarias, la invasión hispana, los estragos del cambio climático que trae sequías y plagas que destruyen la vida, y las diversas con-secuencias e influencias de la emigración. Para los mayas, el maíz siempre ha sido el alimen-to principal y más sagrado. Las historias y recetas dadas en esta visión factual y artista de la comida de los mayas q'anjob'al tienen conexiones continuas con la visión del mundo plasma-da en el Popol Wuj, que afirma que todo lo que existe en la tierra tiene vida, si tiene vida tiene un espíritu y si tiene un espíritu entonces es sagrado, por lo que los abuelos dicen que el maíz es un alimento sagrado que tiene su propio espíritu y energía.
To explore contemporary (from 1990) utilization and practice of electroconvulsive therapy (ECT) worldwide. Systematic search (limited to studies published 1990 and after) was undertaken in the databases Medline, Embase, PsycINFO, SveMed, and EBSCO/Cinahl. Primary data‐based studies/surveys with reported ECT utilization and practice in psychiatric institutions internationally, nationally, and regionally; city were included. Two reviewers independently checked study titles and abstracts according to inclusion criteria, and extracted ECT utilization and practice data from those retrieved in full text. Seventy studies were included, seven from Australia and New Zealand, three Africa, 12 North and Latin America, 33 Europe, and 15 Asia. Worldwide ECT differences and trends were evident, average number ECTs administered per patient were eight; unmodified (without anesthesia) was used in Asia (over 90%), Africa, Latin America, Russia, Turkey, Spain. Worldwide preferred electrode placement was bilateral, except unilateral at some places (Europe and Australia/New Zealand). Although mainstream was brief‐pulse wave, sine‐wave devices were still used. Majority ECT treated were older women with depression in Western countries, versus younger men with schizophrenia in Asian countries. ECT under involuntary conditions (admissions), use of ambulatory‐ECT, acute first line of treatment, as well as administered by other professions (geriatricians, nurses) were noted by some sites. General trends were only some institutions within the same country providing ECT, training inadequate, and guidelines not followed. Mandatory reporting and overall country ECT register data were sparse. Many patients are still treated with unmodified ECT today. Large global variation in ECT utilization, administration, and practice advocates a need for worldwide sharing of knowledge about ECT, reflection, and learning from each other's experiences.
Transformers are a neural network architecture originally developed for natural language processing, which have since become a foundational tool for solving a wide range of problems, including text, audio, image processing, reinforcement learning, and other tasks involving heterogeneous input data. Their hallmark is the self-attention mechanism, which allows the model to weigh different parts of the input sequence dynamically, and is an evolution of earlier attention-based approaches. This article provides readers with the necessary background to understand recent research on transformer models, and presents the mathematical and algorithmic foundations of their core components. It also explores the architecture's various elements, potential modifications, and some of the most relevant applications. The article is written in Spanish to help make this scientific knowledge more accessible to the Spanish-speaking community.
Layout-aware pre-trained models has achieved significant progress on document image question answering. They introduce extra learnable modules into existing language models to capture layout information within document images from text bounding box coordinates obtained by OCR tools. However, extra modules necessitate pre-training on extensive document images. This prevents these methods from directly utilizing off-the-shelf instruction-tuning language foundation models, which have recently shown promising potential in zero-shot learning. Instead, in this paper, we find that instruction-tuning language models like Claude and ChatGPT can understand layout by spaces and line breaks. Based on this observation, we propose the LAyout and Task aware Instruction Prompt (LATIN-Prompt), which consists of layout-aware document content and task-aware instruction. Specifically, the former uses appropriate spaces and line breaks to recover the layout information among text segments obtained by OCR tools, and the latter ensures that generated answers adhere to formatting requirements. Moreover, we propose the LAyout and Task aware Instruction Tuning (LATIN-Tuning) to improve the performance of small instruction-tuning models like Alpaca. Experimental results show that LATIN-Prompt enables zero-shot performance of Claude and ChatGPT to be comparable to the fine-tuning performance of SOTAs on document image question answering, and LATIN-Tuning enhances the zero-shot performance of Alpaca significantly. For example, LATIN-Prompt improves the performance of Claude and ChatGPT on DocVQA by 263% and 20% respectively. LATIN-Tuning improves the performance of Alpaca on DocVQA by 87.7%. Quantitative and qualitative analyses demonstrate the effectiveness of LATIN-Prompt and LATIN-Tuning. We provide the code in supplementary and will release it to facilitate future research.
The lack of wide coverage datasets annotated with everyday metaphorical expressions for languages other than English is striking. This means that most research on supervised metaphor detection has been published only for that language. In order to address this issue, this work presents the first corpus annotated with naturally occurring metaphors in Spanish large enough to develop systems to perform metaphor detection. The presented dataset, CoMeta, includes texts from various domains, namely, news, political discourse, Wikipedia and reviews. In order to label CoMeta, we apply the MIPVU method, the guidelines most commonly used to systematically annotate metaphor on real data. We use our newly created dataset to provide competitive baselines by fine-tuning several multilingual and monolingual state-of-the-art large language models. Furthermore, by leveraging the existing VUAM English data in addition to CoMeta, we present the, to the best of our knowledge, first cross-lingual experiments on supervised metaphor detection. Finally, we perform a detailed error analysis that explores the seemingly high transfer of everyday metaphor across these two languages and datasets.
As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks. In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language processing. We use these tools to measure gender, racial, ethnic, and intersectional social biases across five models trained on emotion regression tasks in English, Spanish, and Arabic. We find that many systems demonstrate statistically significant unisectional and intersectional social biases.
A diferencia del enfoque historiográfico común, este artículo no asume la similitud doctrinaria como la explicación principal de la política comercial. En su lugar, propone considerarla como el resultado de un proceso político que conjuga múltiples intereses. En particular, la política comercial chilena (1850-1914), mediante sus Ordenanzas de Aduanas, ha sido considerada un fiel reflejo de las doctrinas económicas de la época. Sin embargo, este artículo propone ampliar el análisis empírico a las distintas medidas de política, tales como las leyes y decretos, enfatizando su estructura arancelaria ex ante (de iure) más que la ex post (efectiva). Los resultados sugieren un uso compensatorio de los aranceles que llevó a una reducción persistente de estos, independiente de la doctrina imperante en la ordenanza. Sus “designios” fueron, por tanto, más pragmáticos que doctrinarios.
Latin America. Spanish America, Regional economics. Space in economics