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Candidate LLs joining LL associations and networks (ENoLL, Forum LLSA...) undergo quality- assessment processes based on specific criteria and evaluation frameworks to guarantee members meet their LL standards. Currently, limited attention is paid on how such evaluation methods can contribute to future LL-performance. The need to deeper understand the architectural aspects of LLs and further study effective management-approaches has been raised by other authors. Schuurman (2015) proposed a macro–meso-micro-level approach for classifying LLs. Despite its potential value, this three-level analysis approach has not yet been fully recognized in existing LL evaluation frameworks. The adoption of a harmonized macro-meso-micro evaluation approach, with a clear focus on the macro-level could support the development of LL towards sustainability, impact and efficacy. This paper aims to define a set of harmonized weighted criteria for a comprehensive LL evaluation framework to be used for assessing LLs on all three levels based on multi-method research approaches.

https://www.editions-harmattan.fr/index.asp?navig=catalogue&obj=livre&no=62879; National audience; L'innovation est le résultat d'un processus composé de nombreuses étapes et d'interactions entre de multiples personnes, organisations et institutions. Le concept d'écosystème, emprunté à la biologie, traduit bien ces interactions. L'innovation s'épanouit aussi mieux lorsqu'elle est portée par des communautés d'usagers qui sont autant de co-créateurs. Il est également essentiel de faire preuve d'agilité, de partager les risques, de mutualiser les ressources. Pour ces raisons, les écosystèmes d'innovation deviennent le creuset incontournable de l'innovation.

This paper presents some experiments in clustering homogeneous XMLdocuments to validate an existing classification or more generally anorganisational structure. Our approach integrates techniques for extracting knowledge from documents with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging classification. We mix the selection of structured features with fine textual selection based on syntactic characteristics.We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria., Cette version corrige des erreurs dans le nom de 2 auteurs cites dans la bibliographie