The POSCO-India Project and the Land War in Odisha

　In contrast to earlier market-oriented Korean FDIs in India, the POSCO-India project has been embroiled in legal and procedural quagmires and protests from many anti-POSCO groups. Indeed, the project has been one of the most controversial issues in the state, and it has generated a lot of protest. The project ran into trouble from the outset. Villagers were opposed to the acquisition of their land on a fertile strip on the coast of the Bay of Bengal near Paradip, which is famous for its betel vines. Their resistance was largely because the betel-based economy sustained 20,000- odd people in eight villages in Dhinkia, Nuagaon and Gadakujanga gram panchayats, which stood to be affected by the project. The villagers, who gathered under the banner of the POSCO Pratirodh Sangram Samiti (PPSS) to protest the acquisition of their land, rejected the state government’s rehabilitation package. POSCO suspended its project in July 2015 and later decided to temporarily freeze the project in 2016. POSCO confirmed its withdrawal from the project by requesting the Odisha government to take back the land on March 18. So, why did the POSCO-India project fail in Odisha? Why did POSCO decide to invest in India and why did the Odisha government welcome it? What was the origin of the protest against the project, and what was being asked for? The main reasons for the delay and defeat of the project were the failure to build a local political consensus on the project, disputes about government records of the land, and compensation. There were especially strong protests against the project from the prospective displaced persons. Compulsory displacements due to the project unleashed widespread social, economic and environmental changes. Forced displacement epitomizes the social exclusion of certain groups of people. With this background, I examine the failure of the POSCO-India project, the industrial development path and the projectaffected persons (PAPs) discontent in Odisha, and I conclude that the "Land War" in Odisha may not stop for some time

Korean FDI in India: Perspectives on POSCO-India Project

Abstract: This paper reviews the dynamics of POSCO-India project, a combined "mine-steel plant-port" project in Orissa, India during the last five years. In contrast with the market oriented FDI undertaken by Korean companies like LG, Samsung, HMI in India, POSCO-India project is a natural resource seeking FDI that has faced hurdles from the very beginning in setting up its integrated steel plant. Especially there has been strong protest against this Korean project from the prospective displaced persons. The main reasons for the delay in the project are the failure to build local political consensus on the project, regulatory complexities, dispute on government record on the land, and compensation. We conclude that it is important to recognize the difference between the market oriented FDI and resource seeking FDI like Posco-India project. In this regard, the reasonable and fair compensation for the affected peoples is essential. Creating sustainable new employment in the relocation zone is necessary as well. Framing of socially responsible resettlement policy and fostering of local consensus on the cost and benefits of resource seeking FDI projects can be useful in expediting the implementation of such projects

"Mapping" Nonprofit Infrastructure Organizations in Texas

The stability of the nonprofit sector and its ability to meet our nation‘s needs in an era of unprecedented challenges requires a solid nonprofit infrastructure (Brown, et al., 2008). These organizations that comprise this infrastructure system work behind the scenes to provide nonprofit organizations with capacity-building support. However, little is known about the actual infrastructure system, especially at the state and local levels. In order to better understand this system, student researchers from the Bush School of Government and Public Service at Texas A&M University were asked to replicate Dr. David O. Renz‘s 2008 study, ―The U.S. Nonprofit Infrastructure Mapped.‖ The Bush School study focused specifically on the nonprofit infrastructure structure in Texas by categorizing and mapping selected nonprofit organizations using the 11 roles and functions identified by Renz (2008). This report provides a literature review of nonprofit capacity building and organizational infrastructure. In addition, the data collection and classification using Renz‘s 11 roles and functions are detailed and mapping methodology is described. Finally, the researchers offer findings, questions to consider, and recommendations for further research. Findings from this study include: o Urban areas had the largest concentration of infrastructure organizations. Of the 389 nonprofit infrastructure organizations, the largest concentration of organizations was located near Dallas, Houston, Austin, and San Antonio. Several non-metropolitan regions in the state are lacking similar concentrations, even after consideration of the size of the nonprofit sector or general population in the respective regions. o Many organizations performed multiple roles and functions. In one case, one organization performed 10 functions. Many other organizations that were studied performed more than one of the 11 functions. o A large number of infrastructure organizations provide financial support to nonprofits. More than half of the organizations analyzed were categorized as Renz‘s Function Three-Financial Intermediaries because they facilitated the collection and distribution of financial resources to nonprofit organizations. Additionally, 40.4% of the organizations were categorized as Renz‘s Function Four-Funding Organizations because they provided financial resources to nonprofit operating organizations through the distribution of funds from asset pools that they own, manage, and allocate. Future research needs to be Page 4 conducted, however, to determine what proportion of funding is devoted to funding of the other nine Renz categories versus funding to nonprofits providing direct services. It would be useful to consider and respond to categories lacking in such funding, relative to the infrastructure needs of Texas nonprofits generally and also in particular regions of Texas or nonprofit subfields. o Some infrastructure functions were not as apparent. Researchers found that two of Renz‘s functions (Function One-Accountability and Regulation and Function Ten-Research) were performed by less than 5% of the organizations that were analyzed. Recommendations that emerged from this study were: o Regular updates of nonprofit information are important for future research. Nonprofit managers need to be educated about the importance of updating their organization‘s publicly available information. If their website or GuideStar reports are not current, researchers,practitioners, and other constituents cannot accurately analyze the organization. o Nonprofits need to clarify their roles using Renz’s 11 roles and function. Organizations with a mission to support the nonprofit sector should clarify their focus based on the definitions of capacity-building and infrastructure developed by Renz (2008). Do the organizations intend to support the entire nonprofit infrastructure in Texas or only support Function Nine-Capacity Development and Technical Assistance? o Strengthen associations of nonprofit infrastructure organizations throughout the Texas. This action will benefit nonprofit organizations through improved communication among infrastructure organizations, as well as economies of scale and scope. o Facilitate the creation of a network of representatives from each Council of Governments (COG). This organization can serve as a point of contact for matters about the nonprofit infrastructure of that COG.OneStar Foundation, with additional funding from The Meadows Foundatio

MTrainS: Improving DLRM training efficiency using heterogeneous memories

Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training efficiency is becoming considerably more important to keep the data center power demand manageable. In Deep Learning Recommendation Models (DLRM), sparse features capturing categorical inputs through embedding tables are the major contributors to model size and require high memory bandwidth. In this paper, we study the bandwidth requirement and locality of embedding tables in real-world deployed models. We observe that the bandwidth requirement is not uniform across different tables and that embedding tables show high temporal locality. We then design MTrainS, which leverages heterogeneous memory, including byte and block addressable Storage Class Memory for DLRM hierarchically. MTrainS allows for higher memory capacity per node and increases training efficiency by lowering the need to scale out to multiple hosts in memory capacity bound use cases. By optimizing the platform memory hierarchy, we reduce the number of nodes for training by 4-8X, saving power and cost of training while meeting our target training performance