Although the literature on corporate social responsibility has discussed the scope and meaning of CSR extensively, confusion still exists regarding how to define the concept. One controversial issue deals with the changing legal status of CSR. Based on a review of CSR definitions and meta-studies on CSR definitions, we find that the majority of definitions leans toward voluntary CSR. However, some recent regulatory amendments toward mandatory CSR have called into question the established idea of CSR as merely a managerial tool (...) of self-regulation. In this paper, we juxtapose the evolution of CSR in India against the scholarly literature discussing voluntary-versus-mandatory CSR to understand the recent shift toward a new conceptualization of CSR as a form of co-regulation that includes elements of both voluntary and mandatory regulation. The Indian Companies Act 2013 is a remarkable example in that it replaced an older version from 1956, taking a bold step toward the integration of voluntary and mandatory aspects in the application of CSR. We present practical implications of the Indian case for businesses and discuss implications for CSR theory development; we particularly consider the evolution of the business and society relationship from a voluntary soft law approach to CSR to an increasingly hard law approach and transitory hybrid forms in-between like soft–hard law and hard–soft law. (shrink)

Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual (...) representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a na. (shrink)