Being an Early-Career CMS Academic in the Context of Insecurity and ‘Excellence’: The Dialectics of Resistance and Compliance

Drawing on a dialectical approach to resistance, we conceptualise the latter as a multifaceted, pervasive and contradictory phenomenon. This enables us to examine the predicament in which early-career Critical Management Studies academics find themselves in the current times of academic insecurity and ‘excellence’, as gleaned through this group’s understandings of themselves as resisters and participants in the complex and contradictory forces constituting their field. We draw on 24 semi-structured interviews to map our participants’ accounts of themselves as resisters in terms of different approaches to tensions and contradictions between, on the one hand, the interviewees’ Critical Management Studies alignment and, on the other, the ethos of business school neoliberalism. Emerging from this analysis are three contingent and interlinked narratives of resistance and identity – diplomatic, combative and idealistic – each of which encapsulates a particular mode (negotiation, struggle, and laying one’s own path) of engaging with the relationship between Critical Management Studies and the business school ethos. The three narratives show how early-career Critical Management Studies academics not only use existing tensions, contradictions, overlaps and alliances between these positions to resist and comply with selected forces within each, but also contribute to the (re-)making of such overlaps, alliances, tensions and contradictions. Through this reworking of what it means to be both Critical Management Studies scholars and business school academics, we argue, early-career Critical Management Studies academics can be seen as active resisters and re-constituters of their complex field

What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem

Modèles simplifiés par programmation génétique : prise en compte du sens physique

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Critical Learning: An Exploration of the Roles and Development of Early Career CMS Academics

No abstract available

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation