Jean-Pierre Ronfard en autoreprésentation

Jean-Pierre Ronfard a pratiqué plusieurs formes d’autoreprésentation tout au long de sa carrière d’auteur et de metteur en scène. Depuis l’autofiction Tête à tête, en tandem avec Robert Gravel, jusqu’aux Objets parlent, spectacle paradoxal sans comédiens où la main invisible du metteur en scène était fort présente, l’autoreprésentation comme autocritique est demeurée une constante du travail de Ronfard. Passant du dialogue socratique aux envolées rhapsodiques dans ses pièces « sur » le processus créateur, l’auteur a même été tenté par la citation autoréférentielle dans ses autres pièces. Ainsi, il a utilisé la citation tour à tour comme outil de mise en abyme, de digression et d’argument d’appoint, ou encore de véritable figure de métalepse. Bien qu’on n’apprenne rien de l’homme privé, l’exercice d’autoreprésentation consciente auquel s’est livré Ronfard se traduit en véritable autobiographie intellectuelle et morale.Self-representation as auto-critique remained a constant in playwright-director Jean-Pierre Ronfard's work, from Tête à tête—an autofiction developed in tandem with close collaborator Robert Gravel—to the paradoxical take on self-representation, Les objets parlent, an actor-less play where the director's invisible hand dominated. His plays explicitly about the creative process oscillate from Socratic dialogue to Homeric tall-tales, but, surprisingly, his other plays also bear the mark of self-referential quotation. Ronfard thus used self-quotation as a tool for creating theatre within theatre, for insistent digressions, and even as illustration for "living quote". Ronfard didn't reveal much of his private life through his conscious work on self-representation, but rather gave us a moral and intellectual autobiography

The 50 most influential original articles in vascular surgery during the last 25 years

ObjectiveWe have compiled a list of the 50 most-cited original articles in the field of vascular surgery during the last 25 years to highlight the important changes in practice that have occurred during this interval and provide surgical trainees in vascular surgery ready access to such influential articles.MethodsA Web of Knowledge Citation Index Search was performed in December 2013 for the most-cited journal articles in the discipline of vascular surgery. We searched the term “vascular” in the cited reference search area and then further narrowed our results to exclude all categories except “surgery,” “general internal medicine,” and “cardiac/cardiovascular systems.” We included only documents labeled as “articles” and those published in English. Articles dealing with cardiac surgery, interventional cardiology, and cardiovascular biology were excluded. Our search period was from January 1, 1988, through December 3, 2013. The 50 most frequently cited works were chosen, and a citation density was calculated for each, reflecting the average number of citations each received per year since publication. The articles were then sorted into a defined category, based on the clinical subject to which they pertained.ResultsThe Citation Index Search resulted 80,379 articles, of which the top 50 were indexed and organized according to their citation density and area within the scope of clinical vascular surgery. The number of citations ranged from 218 to 3593. The median citation density was 50.2 (range, 11.3-201.3).ConclusionsThis report is a representation of the most-cited original publications in the field of clinical vascular surgery during the last 25 years. This is an effort to highlight the seminal works that have shaped the discipline of vascular surgery as well as to provide a concise reference list for the surgical trainee in the process of his or her education

Application of author bibliographic coupling analysis and author keywords ranking in identifying research fronts of Indian Neurosciences research

Probing research fronts identification unfailingly delivers interesting results in any field due to its decisive nature. Citation analysis is an acclaimed method used in this process among which more successful results backing Author Co-citation Analysis (ACA) and Author Bibliographic Coupling Analysis (ABCA). The current study opted to combine author bibliographic coupling network analysis and author keywords to explore and display a graphical representation of prominent research areas’ evolution over the study period in Indian Neuroscience research domain. Application of hierarchical clustering to author bibliographic coupling networks for all non-overlapping consecutive years included in the study period were performed and analysed in VOSviewer mapping software. The powerful Lin/log modularity normalization was chosen for determining distance based similarity while clustering the network units. Results of the study unfolded ten prominent research subfields with more emphasis on Epilepsy’ and ‘Parkinson’s disease’ research. Depression was identified as one of the upcoming prominent area in recent years. Apart from its cruciality in framing national level mental health policies, the study will also prove ABCA to be an effective method in identifying prominent research areas

Topic detection using paragraph vectors to support active learning in systematic reviews

AbstractSystematic reviews require expert reviewers to manually screen thousands of citations in order to identify all relevant articles to the review. Active learning text classification is a supervised machine learning approach that has been shown to significantly reduce the manual annotation workload by semi-automating the citation screening process of systematic reviews. In this paper, we present a new topic detection method that induces an informative representation of studies, to improve the performance of the underlying active learner. Our proposed topic detection method uses a neural network-based vector space model to capture semantic similarities between documents. We firstly represent documents within the vector space, and cluster the documents into a predefined number of clusters. The centroids of the clusters are treated as latent topics. We then represent each document as a mixture of latent topics. For evaluation purposes, we employ the active learning strategy using both our novel topic detection method and a baseline topic model (i.e., Latent Dirichlet Allocation). Results obtained demonstrate that our method is able to achieve a high sensitivity of eligible studies and a significantly reduced manual annotation cost when compared to the baseline method. This observation is consistent across two clinical and three public health reviews. The tool introduced in this work is available from https://nactem.ac.uk/pvtopic/