En un pueblo obrero : de la privatización al corporativismo

Jorge Durand (1983, [Tesis de maestría])

El presente trabajo como inscrito dentro del proyecto de investigación sobre el desarrollo industrial en Jalisco, pretendía cubrir dos necesidades: avanzar en el conocimiento del proceso de desarrollo industrial de Jalisco desde la perspectiva de la rama textil y a su vez conocer el proceso de formación en consolidación de ese sector de la clase obrera jalisciense. La industria textil mexicana parecería haber sido uno de los ámbitos en que mejor se desarrolló el modelo de “Colonia industrial” que tendrías origen en Inglaterra del siglo pasado, pero que sin embargo no ha sido estudiada como tal. Sobre las formación y consolidación de la clase obrera nos interesaba trazar los orígenes y el proceso de formación de sus organizaciones, sus luchas, demandas, reivindicaciones, en suma, cómo se había constituido y conformado el proletariado textil jalisciense. Investigación tuvo como escenario y marco de análisis la fábrica Río Grande, ubicada en el municipio de El salto, Jalisco. El caso ofrecía la posibilidad de rastrear lo que había sido la colonia industrial y de seguir desde cerca un proceso de formación, consolidación y corporatización sufrido por ese sector de la clase obrera.

Desarrollo Industrial -- Jalisco Industria Textil -- Jalisco Desarrollo Económico -- México Trabajo y Trabajadores -- México HUMANIDADES Y CIENCIAS DE LA CONDUCTA ANTROPOLOGÍA ANTROPOLOGÍA

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

A Flexible, Extensible, Machine-Readable, Human-Intelligible, and Ontology-Agnostic Metadata Schema (OIMS)

Gideon Kruseman (2022, [Artículo])

Reusability Java Script Object Notation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DATA MANAGEMENT INTEROPERABILITY METADATA SOCIOECONOMIC ASPECTS

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Calidad de la democracia en los gobiernos delegacionales de izquierda: Un análisis de las implicaciones de las políticas sociales en la Magdalena Contreras.

MARIELA DIAZ SANDOVAL (2018, [Capítulo de libro])

En México el estudio sobre los gobiernos a nivel delegacional es una gran asignatura pendiente en las ciencias sociales. No obstante, desde diversas disciplinas existe un gran interés en entender las realidades locales. La llegada al poder, los conflictos entre diversas fuerzas políticas en la localidad, las relaciones que se tejen entre ciudadanos, intermediarios y gobernantes, la incidencia de las organizaciones de la sociedad civil, así como el ejercicio del gobierno, y otros aspectos pueden y deben ser analizados con miras a encontrar las diferencias y similitudes en este nivel de estudio.

Educación Derecho a la salud Derecho a la vivienda Sectores vulnerables CIENCIAS SOCIALES CIENCIA POLÍTICA ADMINISTRACIÓN PÚBLICA ADMINISTRACIÓN CIVIL

Nepal Seed And Fertilizer Project

Dyutiman Choudhary (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED FERTILIZERS SEED INDUSTRY PRIVATE SECTOR MAIZE RICE INTEGRATED SOIL FERTILITY MANAGEMENT COVID-19

Nepal Seed And Fertilizer Project

Dyutiman Choudhary (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SEED INDUSTRY PRIVATE SECTOR MAIZE RICE INTEGRATED SOIL FERTILITY MANAGEMENT COVID-19

Agricultural value chains in Sudan: an annotated bibliography

Hugo De Groote Paswel Marenya (2023, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL VALUE CHAINS CASH CROPS FOOD CROPS LIVESTOCK SOCIOECONOMIC ASPECTS GENDER SOCIAL INCLUSION POSTHARVEST LOSSES BIBLIOGRAPHIES

Value chain research and development: The quest for impact

Jason Donovan (2023, [Artículo])

Motivation: For decades, governments, donors, and practitioners have promoted market-based development approaches (MBDA), most recently in the form of value chain development (VCD), to spur economic growth and reduce poverty. Changes in approaches have been shaped by funders, practitioners and researchers in ways that are incompletely appreciated. Purpose: We address the following questions: (1) how have researchers and practitioners shaped discussions on MBDA?; and (2) how has research stimulated practice, and how has practice informed research? We hypothesize that stronger exchange between researchers and practitioners increases the relevance and impact of value chain research and development. Methods and approach: We adopt Downs' (1972) concept of issue-attention cycles, which posits that attention to a particular issue follows a pattern where, first, excitement builds over potential solutions; followed by disenchantment as the inherent complexity, trade-offs, and resources required to solve it become apparent; and consequently attention moves on to a new issue. We review the literature on MBDA to see how far this framing applies. Findings: We identify five cycles of approaches to market-based development over the last 40 or more years: (1) non-traditional agricultural exports; (2) small and medium enterprise development; (3) value chains with a globalization perspective; (4) value chains with an agri-business perspective; and (5) value chain development. The shaping and sequencing of these cycles reflect researchers' tendency to analyse and criticize MBDA, while providing limited guidance on workable improvements; practitioners' reluctance to engage in critical reflection on their programmes; and an institutional and funding environment that encourages new approaches. Policy implications: Future MBDA will benefit from stronger engagement between researchers, practitioners, and funders. Before shifting attention to new concepts and approaches, achievements and failures in previous cycles need to be scrutinized. Evidence-based practice should extend for the length of the issue-attention cycle; preferably it should arrest the cycling of attention. Funders can help by requiring grantees to critically reflect on past action, by providing “safe spaces” for sharing such reflections, and by engaging in joint learning with practitioners and researchers.

Agri-Food Value Chains Issue-Attention Cycles Market-Based Development Approaches CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA VALUE CHAINS PRIVATE SECTOR RURAL DEVELOPMENT SMALLHOLDERS

Teacher training in the state of Chihuahua: Between the health challenge and teacher resilience

Evangelina Cervantes Holguín Pavel Roel Gutiérrez Sandoval Cely Celene Ronquillo Chávez (2023, [Artículo, Artículo])

The article proposes to recover the response of the Teacher Training and Updating Institutions in the state of Chihuahua regarding the various challenges imposed by the Coronavirus Disease (COVID-19). The qualitative exercise analyzes the experience of 10 institutions based on the voice of their students, teachers, and principals regarding changes in academic, administrative, and organizational processes. It is concluded that the pandemic has affected each institution in different ways and with diverse intensity. Despite the achievements, the experience analyzed reveals the relative success of the using virtual platforms in the face of three basic conditions: connectivity, technological competencies, and socio-emotional skills of the teaching staff. It highlights the importance of implementing tutoring, resilience, or awareness actions of teachers and students' needs, feelings, and sufferings. It is opportune to recover the experiences of other institutions and to question especially students, thesis students and graduates.

Acceso a la educación Aprendizaje en línea Educación a distancia Formación de docentes Tecnología educacional HUMANIDADES Y CIENCIAS DE LA CONDUCTA HUMANIDADES Y CIENCIAS DE LA CONDUCTA Access to education online learning distance education teacher education educational technology