Abstract

Manuguru, located in Telangana, India, is a significant coal mining hub in the Godavari Valley Coalfield, with over a century of mining history. The region, experiencing rapid industrialization and urbanization, undergoes comprehensive landscape changes, impacting natural ecosystems, agricultural lands, and water resources beyond the coal-bearing areas. This study presents an analysis of the dynamic land use and land cover (LULC) patterns in Manuguru, utilizing multi-spectral/multi-temporal Landsat satellite imagery from 1987 to 2020. Focused on understanding the impact of mining activities on the region's landscape, advanced band indices, namely, Normalized Difference Vegetation Index( NDVI) and Normalized Difference Water Index(NDWI) are employed alongside visual interpretation techniques helps validate these indices by visually inspecting the actual features on the ground.. The interpretation of Landsat images helped delineate 8 land cover categories: agricultural land, barren land, forest, mining sites, built-up areas, rivers, stagnant water bodies, and dried water bodies. In addition to traditional analysis methods, the study incorporates rate of change calculations and transition matrices to quantify and understand the temporal dynamics of LULC composition. Comparative analysis reveals significant shifts in LULC composition over the study period where the expansion of coal mining activities emerges as a dominant driver of change. This resulted in the loss of ecologically rich forests and the proliferation of overburden hills. Despite the prevalent industrial activities, there is a visible dedication to environmental stewardship by reclaiming overburden dumps through reforestation efforts of open scrub areas. The transition of agricultural lands to built-up areas signifies rapid urban expansion, driven by burgeoning population and economic activities. Similarly, the transformation of water bodies into agricultural lands underscores the human demand for arable land, often at the expense of natural ecosystems. The combined effect of transitions within the land use classes can be attributed to the increase of extractive industries and urbanization.

Abstract

Manuguru, a historic coal mining hub in India's Godavari Valley Coalfield, embodies the intricate connections between resource extraction and its wide-ranging impacts. For over a century, mining has carved into the land, leaving a legacy beyond mere geographic alterations. It has reshaped ecosystems, reshuffled communities, and fueled socio-economic changes, often along lines of caste and ethnicity. This study delves into this transformative process through a geospatial analysis of land use and land cover (LULC) changes from 1987-2020, utilizing multi-spectral/multi-temporal Landsat satellite imagery. With a focus on mining's impact, advanced band spatial indices which are coupled with visual interpretation, unveil eight distinct land cover categories: agricultural land, barren land, forest, mining sites, built-up areas, rivers, stagnant water bodies, and dried water bodies, which provide a quantitative understanding of temporal dynamics. Beyond the geographical, field work delves into the human story. We explore the socio-economic realities of Manuguru, examining how mining has intensified existing divisions and fostered caste-based tensions. We further investigate how public policies intended for development might have inadvertently contributed to spatial segregation. By integrating geospatial insights with social realities, this study underscores the complex interplay between extractive industries, environmental concerns, and the social fabric. Understanding these dynamics is crucial for informed decision-making toward a sustainable future for Manuguru and similar resource-rich landscapes, one that balances progress with social equity and environmental well-being.

Abstract

Historians and economists working on the Indian railways refer to what they term the ‘railway mania’ of Britain in the 1840s, resulting ultimately in two principal achievements of the industrial revolution (the steamship and the steam engine) being brought to the colonies.1 Multiple rationales have been ofered to explain the spread of this ‘mania’ to India. Recounting the debate around the Indian railways, Bipan Chandra reminds us of the ‘strong political and economic pressure’ from textile manufacturers in Lancashire, who wished to access the vast supplies of cotton and wheat in India.2 Apart from being a source of opulent raw materials, India could also become a huge consumer of textile and other products manufactured in Britain, Daniel Thorner points out.3 In addition to the commercial and economic advantages, once put in place, the railways could be used to transport military personnel from one part of the vast colony to another in a relatively short time.4 These economic and political justifcations apart—many of which have been dealt with in great detail by a number of scholars of colonial India—we are left with the undeniable fact that railway construction remained a crucial endeavour of colonial rule in British India. The frst passenger railway line in India, covering 34 km between Bori Bunder (Bombay) and Thane, was opened to trafc on 16 April 1853, barely 28 years after the world’s frst successful train ran between Stockton and Darlington in England in 1825. This was one of the frst railway lines in the country. Before passenger trains were introduced, there were a few trains used to transport stone and other material, such as on the Red Hill line in Chennai in South India. The frst short lines were followed by a rapid escalation of railway construction.5 In Ian Kerr’s estimate, by 1870, ‘excluding Russia (and thus Russian Asia), ffty-fve per cent of all operating railway mileage in Africa, Asia, and Latin America combined was located in India’. The fact that neither China nor Japan had operating lines at the time stresses the unique nature of railway construction in India.6 During these initial tumultuous years, the railway building project was

Abstract

The Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 are designed to further empower the state and allow the executive considerable powers to shape public discourses. On the one hand, the state now demands access to all information about the content and origins of every digital communication, a measure that will weaken the right to privacy. On the other hand, digital content is now subject to both self-regulation as well as extensive surveillance and regulation designed to allow substantial control by the executive over content.

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multilabel classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and countering sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best propose method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.