Intersectionality and mixed methods for social context in entrepreneurship

Purpose This conceptual paper has two central aims: to critically analyse the potential of intersectionality theory as a means by which to understand aspects of context in entrepreneurship studies, and advocate for the value of a realist perspective and mixed methods approaches to produce better intersectional research on entrepreneurship. Design/methodology/approach Highlighting context as an emerging topic within entrepreneurship literature, the paper examines how drawing upon notions of intersectionality and positionality can help to explain the social context for entrepreneurial activity and outcomes, particularly in terms of agency and resources. Findings The paper complements and extends existing intersectional approaches to entrepreneurship studies by introducing Archer’s critical realist philosophical perspective on agency and Anthias’ positional perspective on resource access, considering the usefulness of realism and mixed methods approaches for such work, and outlining a methodologically informed potential research agenda for the area. Originality/value The paper offers a theoretical foundation for researchers to begin systematically exploring social entrepreneurial context by accounting for the effects of overarching intersecting structures such as gender, race, and socio-economic class (amongst others), presents empirical methods through which these social-structural influences, and the degree of their impact, can be identified and analysed, and suggests a philosophically robust means of conceptualising how, in combination with agency, they influence essential aspects of entrepreneurial activity

Layer-wise Learning of Kernel Dependence Networks

Due to recent debate over the biological plausibility of backpropagation (BP), finding an alternative network optimization strategy has become an active area of interest. We design a new type of kernel network, that is solved greedily, to theoretically answer several questions of interest. First, if BP is difficult to simulate in the brain, are there instead "trivial network weights" (requiring minimum computation) that allow a greedily trained network to classify any pattern. Perhaps a simple repetition of some basic rule can yield a network equally powerful as ones trained by BP with Stochastic Gradient Descent (SGD). Second, can a greedily trained network converge to a kernel? What kernel will it converge to? Third, is this trivial solution optimal? How is the optimal solution related to generalization? Lastly, can we theoretically identify the network width and depth without a grid search? We prove that the kernel embedding is the trivial solution that compels the greedy procedure to converge to a kernel with Universal property. Yet, this trivial solution is not even optimal. By obtaining the optimal solution spectrally, it provides insight into the generalization of the network while informing us of the network width and depth