The article seeks to fill a gap in existing scholarship on explicit and implicit linguistic, ethnic, and national classifications in Habsburg schools and their effects. It attempts to reconstruct the classifications that appeared in textbooks and other teaching materials as well as in daily practice, pupils’ exposure to them, and their engagement with these categories. Temporally, the study begins with the establishment of compulsory education (1774) and ends in the revolutionary period of 1848–49 and focuses on the Slavophone population of the Habsburg crownlands Carniola, Carinthia, Styria, and the Austrian Littoral. Our research suggests that systematic and uniform classification schemes were not yet in place in the school environment in the period under review. As a result, the influence of classificatory systems on identifications was limited. If anything, the schools inadvertently reproduced existing local and provincial identifications while the students’ limited internalization of emerging transregional identifications only happened through their personal relationships with a few teachers and peers. The transition from familiarization with emerging transregional ethnolinguistic identifications through personal networks to the systemic (and often completely unintentional) reproduction of nationalist ideology happened only after 1848.

Goal-setting is fundamental to organisational management, yet not every manager knows how to do it well. A narrative literature review was done to explore current knowledge of definitions and classifications of goals, and principles of goal-setting in the healthcare sector. Online databases generated 65 relevant articles. Additional literature sources were snowballed from referenced articles, and textbooks. Most academic authors define ‘goal’ synonymously as ‘aim’ or ‘objective’, but there is evidence of hermeneutical confusion in general literature. Goal classifications are diverse, differing according to their contextual, structural, functional, and temporal characteristics. Many authors agree that goal-setting is problem-based, change-oriented, and can effectively motivate attainment if the goal statement is formulated with a specific and challenging or SMART framework. However, recent authors report varying definitions for SMART, and evidence of past studies that empirically examined the nature and efficacy of frameworks currently used for formulating goal statements for health programmes are lacking.

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

L’objectif général de cette recherche a consisté à apprécier le degré de concordance entre un système de classification imposé (le DSM III) et un système de classification naturel ou intuitif. Pour ce faire, on a mis en correspondance des observations en langage naturel (diagnostic en clair) fournies par 158 psychiatres différents sur 999 malades psychiatriques avec les diagnostics établis selon les critères du DSM III. Dans cet échantillon, 823 malades ont relevé d'un traitement antidépresseur; ils ont été répartis en 7 groupes de dépression (majeure simple isolée, mélancolique, bipolaire, atypique, atypique avec symptômes obsessionnels, avec alcoolisme, avec symptômes obsessionnels et alcoolisme) et un groupe répondant au diagnostic de schizophrénie. Les observations en langage naturel ont été traitées de façon entièrement automatique par le programme SPAD. Ont été analysées successivement la fréquence de certains mots clés, les formes graphiques (lexicales) caractéristiques et les phrases modales prononcées par les psychiatres à propos de chaque groupe de malades. Ces données ont été synthétisées dans l'analyse factorielle lexicale qui fournit une représentation simultanée des formes graphiques et des groupes diagnostiques. Les résultats font apparaître une concordance satisfaisante entre les diagnostics du DSM III et les observations en langage naturel à l'exception cependant de la dépression majeure et de la mélancolie qui se trouvent quasiment confondues dans les descriptions. Ces résultats ont été discutés en termes d'interactions entre systèmes diagnostiques imposés et systèmes diagnostiques spontanés.