Etnografia de tela e semiopragmática: um diálogo entre metodologias de análise fílmica.

The “screen ethnography” is a proposal of analysis presented by the brazilian researcher Carmen Rial (2004) based on studies in the field of anthropology and has as its starting point the changes promoted by the poststructuralist bias in the field of humanities studies, such as decolonial theories, queer theory, and many others. Its methodology proposes the application of proper procedures of anthropological research - such as the researcher’s long immersion in the field, systematic observation, field notebook registration, etc. - as a method resource in the study of media productions, including cinema. The “semio-pragmatic” is an analytical model proposed by french researcher Roger Odin (2000) inspired by the contributions of semiology/semiotics to film analysis - a structuralist method - associated with a pragmatic perspective in such way the film’ narrative is considered according to conditions available the context of its reception. As a point of convergence, the two methodological proposals emphasize the importance of context in the analytical process of films. While screen ethnography considers the context of research as relevant information to conduct filmic analysis, semio-pragmatics emphasizes the context of reception in which the viewer is inserted at the moment of exhibition. By providing a synthesis of the main characteristics and strategies of each methodological proposal, our aim in this presentation is to contribute to a reflection on the modes of context appropriation in the film analysis process and its implications that each methodological choice has on the final result of the analysis.

Development of a Taxonomy of Workplace Bullying Intervention Types: Informing Research Directions and Supporting Organizational Decision Making

Recently much has been done to increase our understanding of workplace bullying including its causes, consequences, and prevalence. Although identification of possible interventions has advanced the field, systematic evidence on the efficacy and effectiveness of specific interventions is lacking. At the same time, organizations are under increasing pressure to prevent and manage workplace bullying more effectively. The aim of this study is to develop and refine a taxonomy of workplace bullying intervention types. Although it does not assess the evidence base of interventions per se, the taxonomy is designed to support the development of research into the future and guide organizations in their decision making when implementing interventions. Using a two-round Delphi process, the expertise of international academics and practitioners ( n = 51 and n = 39) was sought to refine the taxonomy, which classified interventions in terms of mode, focus, agent, specificity, and timecourse. Eleven core intervention types were endorsed as appropriate inclusions in the taxonomy of workplace bullying intervention types. A further six, including mediation, failed to reach consensus among the expert panel. The resulting taxonomy forms a framework to guide workplace bullying intervention efficacy and effectiveness research from a common understanding of the definition, scope, and properties of interventions. Once this occurs, the taxonomy can be used by organizations to audit their existing programs, prioritize new interventions, and consider alternate options. Intervention types that did not reach consensus highlight areas of particular research need, which, when undertaken will add to an evidence base and will be included in future versions of the taxonomy.

Automation of citation screening in pre-clinical systematic reviews

BackgroundThe amount of published in vivo studies and the speed researchers are publishing them make it virtually impossible to follow the recent development in the field. Systematic review emerged as a method to summarise and analyse the studies quantitatively and critically but it is often out-of-date due to its lengthy process.MethodWe invited five machine learning and text-mining groups to build classifiers for identifying publications relevant to neuropathic pain (33814 training publications). We kept 1188 publications for the assessment of the performance of different classifiers. Two groups participated in the next stage: testing their algorithm on datasets labeled for psychosis (11777/2944) and datasets labeled for Vitamin D in multiple sclerosis (train/text: 2038/510).ResultThe performances (sensitive/specificity) of the most promising classifier built for neuropathic pain are: 95%/84%. The performance for psychosis and Vitamin D in multiple sclerosis datasets are 95%/73% and 100%/45%.ConclusionsMachine learning can significantly reduce the irrelevant publications in a systematic review, and save the scientists’ time and money. Classifier algorithms built for one dataset can be reapplied on another dataset in different field. We are building a machine learning service at the back of Systematic Review & Meta-analysis Facility (SyRF).

Divergence and Vorticity from Aircraft Air Motion Measurements

This paper considers the accuracy of divergence estimates obtained from aircraft measurements of the horizontal velocity field and points out an error that appears in these estimates that has heretofore not been addressed. A procedure for eliminating this error is presented. The divergence and vorticity are estimated from the coefficients of a least squares fit to a wind field obtained from the Second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) circular flight legs. These estimates are compared with estimates from numerical models and satellites and with airplane estimates based on tracer budgets and the temporal changes in cloud-top height. The estimates are consistent with expectations and estimates using other methods, albeit somewhat high. Furthermore, significant differences occur among the cases, likely due to the large differences in the techniques. The results indicate that the wind field technique is a viable approach for estimating mesoscale divergence if the wind measurements are accurate. The largest source of wind field systematic error may be the result of flow distortion effects on the air velocity measurement and limitations of in-flight calibrations. Because of flow distortion, the only way the current systems can be calibrated is by flight maneuvers, which assume a steady-state homogeneous nonturbulent atmosphere. Analysis of the errors in this technique suggests that wind field measurements with minimal systematic errors should provide estimates of divergence with much greater accuracy than is now possible with other existing methods.