1,720,954 research outputs found
Implementation of an Artificial Intelligence (AI) Model to Enhance Recruitment Processes at El Bosque University: A Predictive Analysis-Based Intervention
No full textEste artículo presenta los resultados de una intervención realizada mediante la aplicación de un modelo de inteligencia artificial por medio de aprendizaje automático Random Forest como herramienta predictiva en procesos de selección de personal dentro del contexto educativo, específicamente en la Universidad El Bosque. La intervención se desarrolló con el fin de demostrar su aplicabilidad en otros contextos diferentes a los industriales, donde generalmente ha sido probada y aplicada, así como para aportar en la automatización de procesos del área de talento humano sin mayores inversiones económicas y aprovechando las herramientas tecnológicas que están a disposición en la actualidad. La investigación se realizó como una intervención aplicada en la cual inicialmente se diseñó y estructuró una base de datos con variables clave como edad, género, nivel educativo, así como habilidades blandas, competencias, valoración obtenida y cargo aspirado. Luego, la base de datos se depuró en Excel® y se entrenó un modelo Random Forest utilizando el lenguaje de programación Python, incluyendo técnicas de análisis supervisado. De acuerdo con este entrenamiento, los resultados evidenciaron un desempeño sobresaliente del modelo, con una precisión del 97.3%, lo que indica que tiene una alta capacidad para identificar correctamente candidatos aptos y no aptos aplicando los criterios establecidos. Las conclusiones muestran que es viable la implementación de herramientas de IA, especialmente el aprendizaje automático, en la gestión del talento humano en contextos universitarios, en fases de selección inicial. Sin embargo, es necesario garantizar la calidad de los datos, la ética en su uso y la explicabilidad del modelo para una implementación efectiva en convocatorias reales de selección de personal. Finalmente, se plantean diversas líneas de investigación futura orientadas a fortalecer su aplicabilidad, integración institucional y evaluación del impacto organizacional.This article presents the results of an intervention carried out through the application of an artificial intelligence model using the Random Forest machine learning algorithm as a predictive tool in recruitment processes within the educational context, specifically at Universidad El Bosque. The intervention was developed to demonstrate its applicability in contexts beyond the industrial sector—where it has been most commonly tested and applied—and to contribute to the automation of Human Talent processes without requiring significant financial investment, leveraging currently available technological tools.
The research was conducted as an applied intervention. Initially, a database was designed and structured with key variables such as age, gender, educational level, as well as soft skills, competencies, evaluation scores, and the position applied for. The database was then cleaned using Excel®, and a Random Forest model was trained using the Python programming language, incorporating supervised learning techniques.
Based on this training, the results showed outstanding model performance, with an accuracy rate of 97.3%, indicating a high ability to correctly identify suitable and unsuitable candidates according to the established criteria. The conclusions suggest that implementing AI tools—particularly machine learning—in Human Talent management is feasible in university contexts, especially in early-stage recruitment. However, it is essential to ensure data quality, ethical use, and model explainability to enable effective implementation in real recruitment calls.
Finally, several lines of future research are proposed, aimed at strengthening the model’s applicability, institutional integration, and evaluation of its organizational impact
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Benefits and challenges of artificial intelligence in recruitment and selection: a literature review
No full textLa inteligencia artificial (IA) está transformando los procesos de reclutamiento y selección de personal, ofreciendo herramientas que prometen mejorar la eficiencia, reducir sesgos y optimizar la identificación de talento. Este artículo presenta una revisión de literatura con el objetivo de explorar y evaluar los beneficios y desafíos asociados con la implementación de la inteligencia artificial (IA) en los procesos de reclutamiento y selección de personal, abarcando estudios publicados entre 2019 y 2024. Se utilizaron métodos de análisis bibliométrico con un enfoque cuantitativo de procesamiento y análisis para sintetizar los resultados, los cuales indican que la inteligencia artificial (IA) tiene un impacto en la mejora de la eficiencia y precisión de los procesos de reclutamiento, permitiendo identificar candidatos ideales de manera más ágil y reduciendo costos operativos. Se encontró que la IA contribuye a una mayor objetividad en la selección al minimizar los sesgos humanos, aunque los desafíos éticos, como la posible discriminación algorítmica y la falta de transparencia, siguen siendo preocupaciones importantes. Además, los candidatos perciben positivamente los procesos mediados por IA, siempre que se garantice la equidad y se comuniquen claramente los mecanismos de decisión. Los estudios también evidencian la importancia de la colaboración entre humanos e IA, destacando que un enfoque híbrido puede equilibrar la eficiencia tecnológica con el juicio ético y humano. Sin embargo, se identificaron vacíos en la literatura relacionados con la adopción de IA en diferentes contextos culturales y sectores económicos, así como con el impacto a largo plazo en la retención de talento y la satisfacción de los empleados.Artificial intelligence (AI) is transforming recruitment and selection processes, offering tools that promise to improve efficiency, reduce bias, and optimize talent identification. This article presents a literature review with the aim of exploring and evaluating the benefits and challenges associated with the implementation of artificial intelligence (AI) in recruitment and selection processes, covering studies published between 2019 and 2024. Bibliometric analysis methods with a quantitative processing and analysis approach were used to synthesize the results, which indicate that artificial intelligence (AI) has an impact on improving the efficiency and accuracy of recruitment processes, allowing ideal candidates to be identified more quickly and reducing operational costs. AI was found to contribute to greater objectivity in selection by minimizing human biases, although ethical challenges, such as potential algorithmic discrimination and lack of transparency, remain major concerns. In addition, candidates positively perceive AI-mediated processes, if fairness is guaranteed, and decision mechanisms are clearly communicated. The studies also highlight the importance of human-AI collaboration, highlighting that a hybrid approach can balance technological efficiency with ethical and human judgment. However, gaps were identified in the literature related to AI adoption in different cultural contexts and economic sectors, as well as the long-term impact on talent retention and employee satisfaction
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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