La metáfora visual corporeizada: bases cognitivas del discurso audiovisual

Las teorías cognitivas han demostrado que el pensamiento humano se encuentra corporeizado; es decir, que accedemos a la realidad mediante nuestros sentidos y no podemos huir de ellos. Para entender y manejar conceptos abstractos utilizamos proyecciones metafóricas basadas en sensaciones corporales. De ahí la ubicuidad de la metáfora en el lenguaje cotidiano. Aunque esta afirmación ha sido ampliamente probada con el análisis del corpus verbal en distintas lenguas, apenas existen investigaciones en el corpus audiovisual. Si las metáforas primarias forman parte de nuestro inconsciente cognitivo, son inherentes al ser humano y consecuencia de la naturaleza del cerebro, deben generar también metáforas visuales. En este artículo, se analizan y discuten una serie de ejemplos para comprobarlo.

International conference paper presentations: A multimodal analysis to determine effectiveness

International conference presentations represent one of the biggest challenges for academics using English as a Lingua Franca (ELF). This paper aims to initiate exploration into the multimodal academic discourse of oral presentations, including the verbal, written, non-verbal material (NVM) and body language modes. It offers a Systemic Functional Linguistic (SFL) and multimodal framework of presentations to enhance mixed-disciplinary ELF academics' awareness of what needs to be taken into account to communicate effectively at conferences. The model is also used to establish evaluation criteria for the presenters' talks and to carry out a multimodal discourse analysis of four well-rated 20-min talks, two from the technical sciences and two from the social sciences in a workshop scenario. The findings from the analysis and interviews indicate that: (a) a greater awareness of the mode affordances and their combinations can lead to improved performances; (b) higher reliance on the visual modes can compensate for verbal deficiencies; and (c) effective speakers tend to use a variety of modes that often overlap but work together to convey specific meanings. However, firm conclusions cannot be drawn on the basis of workshop presentations, and further studies on the multimodal analysis of ‘real conferences’ within specific disciplines are encouraged.

Interpersonal relations and nurses' job satisfaction through knowledge and usage of relational skills

Background/rationale: Many interpersonal labor disputes stem from the lack of communication skills and the relational problems in the interactions between health professionals. Aims/methods: A qualitative study was conducted in a Spanish hospital in order to get to know how the communicative interaction between hospital nurses is like in relation to the nurses' interpersonal interaction and communication skills developed in their working relationships. Twenty-one hospital nurses between 29 and 55 years old, working in different wards, were interviewed. Open-ended interview discourses were transcribed verbatim and analyzed using qualitative content analysis. Results: The following four key themes were analyzed: communication and sender; communication and awareness of who has the problem; non-verbal communication; communication and recipient. Conclusion: The results of this study highlight the need to broaden nurses' relational–communication skills in order to increase job satisfaction.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.