Ravidas community problems in the West Midlands and Northern India

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Human resource management in China and India

The first decade of the twenty-first century has witnessed further growth in emerging markets, which is significantly influencing the global economic landscape. For the first time in almost two hundred years, it is in this decade that the emerging economies have caught up with, and raced ahead of, the developed ones in terms of gross domestic product. This is a trend that is likely to continue for some time as many of the developed economies struggle to recover from the global financial crisis. In particular, China and India as two fast growing economies are significantly contributing to the world economic growth and are the flag bearers of this transformation. Acknowledged as favourite destinations for global manufacturing (China) and services (India) related outsourcing, both nations offer huge growth opportunities in most products and services. However, in order to sustain their phenomenal economic growth of the past decades, both countries are facing a number of challenges to their human resource management (HRM). From a macro perspective, these issues tend to appear similar (e.g., attraction and retention of talent), but given the significant sociocultural, institutional, political, legal and other differences between the two nations, the logics underpinning the approaches to managing human resources issues appear somewhat different. This chapter therefore aims to highlight the key forces determining the nature of HRM in China and India. The chapter consists of three main sections, in addition to the Introduction and Conclusions.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.