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.

Abstract

This paper looks at the responses of organised labour to the “development” project exemplified by the steel plant, the big dam and the power plant. More precisely, it analyses the presence (or not) of an ecological critique in trade union/labour responses to industrial projects. The paper explores theoretical connections between class-based politics and ecological questions. It delineates some of the specific processes and conditions which can facilitate the entry of ecological issues into the imaginations of a trade union, using the experiences of the Chhattisgarh Mukti Morcha in central India in the process. It argues that “class consciousness” is hardly a deterrent for an engagement with ecology, if one adequately understands the definitions of “class,” labour and production. In India’s specific context, the coming together of the concerns and imaginations of the industrial worker and the Adivasi peasant/farmer can facilitate this conversation between “red” and “green” imaginations.

Abstract

This paper traces the nature of resistance in movements against land acquisition in Jagatsinghpur and Niyamgiri in the state of Odisha in India. At both sites, the movements were united in their opposition to the respective industrial projects, and to the state's notion of ‘development’ premised on resource extraction and large industry. At certain key junctures, the Niyamgiri resistance placed stress on the ‘sacred’ and the spiritual, while the resistance in Jagatsinghpur emphasised ‘traditional’ land- and water-based economic livelihoods. This paper uses these case studies to understand the role of overt and covert ‘performances’ of indigeneity in shaping environmental discourse as well as the state's response to resistance movements.