Mario Graff

This article analyzes the degree of public policy integration in the federal governance of food security in Mexico. Drawing on the policy integration framework proposed by Candel and Biesbroek, the study examines four key dimensions: (1) policy frame, (2) subsystem involvement, (3) policy goals, and (4) policy instruments. The analysis reveals a partial recognition of the multifunctional nature of food security, but this recognition does not translate into fully integrated policy design or implementation. Instead, food security interventions remain fragmented, concentrated within a limited number of institutions, and disproportionately focused on food utilization, to the detriment of availability and accessibility. Weak coordination across sectors and levels of government is further reinforced by a highly centralized financial architecture, which constrains policy coherence, limits instrument complementarity, and restricts the capacity of subnational actors to engage in coordinated action. Although many initiatives are formally aligned with national and international frameworks, implementation often remains rhetorical rather than operational. To address these limitations, the article argues for a shift toward polycentric governance supported by a reconfigured financial architecture and balanced policy instrument mixes. Finally, the study highlights the role of evaluation as a critical mechanism for advancing toward more integrated, coherent, and sustainable food security policies in Mexico. 摘要 本文分析了墨西哥在联邦层面治理粮食安全时的公共政策整合程度。基于Candel和Biesbroek提出的政策整合框架, 本研究考察了四个关键维度:(1)政策框架, (2)子系统参与, (3)政策目标, 以及(4)政策工具。分析表明, 尽管人们在一定程度上认识到了粮食安全的多功能性, 但这种认识并未转化为完全整合的政策设计或实施。相反, 粮食安全干预措施依然处于碎片化状态, 集中在少数几个机构手中, 且过度侧重于“粮食利用”环节, 从而忽视了“粮食供应”与“获取能力”方面的问题。高度集中的财政体制进一步加剧了跨部门及跨层级政府间协调不力的问题, 这不仅制约了政策的一致性与工具间的互补性, 还限制了地方主体开展协同行动的能力。尽管许多举措在形式上与国内及国际框架保持一致, 但在实际执行中往往流于形式, 缺乏实质性的操作落实。针对上述局限性, 本文主张转向多中心治理模式, 并辅以重构的财政体制与均衡的政策工具组合。最后, 本研究强调了评估机制的关键作用, 认为它是推动墨西哥建立更具整合性、一致性和可持续性的粮食安全政策体系的重要手段。

In recent decades, challenges have become very popular in scientific research as these are crowdsourcing schemes. In particular, challenges are essential for developing machine learning algorithms. For the challenges settings, it is vital to establish the scientific question, the dataset (with adequate quality, quantity, diversity, and complexity), performance metrics, as well as a way to authenticate the participants' results (Gold Standard). This paper addresses the problem of evaluating the performance of different competitors (algorithms) under the restrictions imposed by the challenge scheme, such as the comparison of multiple competitors with a unique dataset (with fixed size), a minimal number of submissions and, a set of metrics chosen to assess performance. The algorithms are sorted according to the performance metric. Still, it is common to observe performance differences among competitors as small as hundredths or even thousandths, so the question is whether the differences are significant. This paper analyzes the results of the MeOffendEs@IberLEF 2021 competition and proposes to make inference through resampling techniques (bootstrap) to support Challenge organizers' decision-making.

Spanish is one of the most spoken languages in the world. Its proliferation comes with variations in written and spoken communication among different regions. Understanding language variations can help improve model performances on regional tasks, such as those involving figurative language and local context information. This manuscript presents and describes a set of regionalized resources for the Spanish language built on 4-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities and examples of using regional resources on message classification tasks.

With the recent growth in food-delivery applications, creating new recommendation systems tailored to this platform is essential. State-of-the-art restaurant recommendation systems are based on users’ ratings or reviews, with data that are obtained from questionnaires or online platforms such as TripAdvisor, Zomato, Foursquare, or Yield. However, not all users give ratings or reviews after their purchase. This document proposes a recommendation system whose input is the number of orders stored by a real food-delivery application. These data are always available for all food-delivery applications and are stored all the time. Our proposal is based on the nearest-neighbor technique that calculates the client’s preferred restaurants and analyzes other clients with similar buying patterns. In addition, we propose a performance metric that can be used for this specific recommendation system that is based on real restaurant sales. We use a real dataset (available online) to validate our proposal. Based on our experiments, the recommendation system successfully gives only an average of 7.7 options from 187 that are available. We compared our proposal with other state-of-the-art recommendation techniques and obtained a better performance. Our results indicate that it is possible to generate recommendations based on the number of orders, making the use of a restaurant-recommendation system feasible in a real food-delivery application.

Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals’ semantics for choosing parents that enhance the addition, Naive Bayes, and Nearest Centroid. To the best of our knowledge, this is the first time that functions’ properties are used for guiding the learning process. As the heuristics were created based on the properties of these functions, we apply them only when they are used to create offspring. The similarity functions considered are the cosine similarity, Pearson's correlation, and agreement. We analyze these heuristics’ performance against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of parent selection based on agreement and random selection to replace an individual in the population produces statistically better results than the classical selection and state-of-the-art schemes, and it is competitive with state-of-the-art classifiers. Finally, the code is released as open-source software.