India's Winter of Discontent: Some Feminist Dilemmas in the Wake of a Rape

Dutta, Debolina; Sircar, Oishik

doi:10.1353/fem.2013.0023

Cited by 87 publications

(2 citation statements)

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“…Under the direction of a social scientist, 23 categories of sexism were formulated taking into account gender-related discourse and campaigns (Dutta and Sircar, 2013;Eccles et al, 1990;Mead, 1963;Menon, 2012) as well as possible impact on public policy. Table 1 provides succinct descriptions for the categories.…”

Section: Dataset Constructionmentioning

confidence: 99%

Multi-label Categorization of Accounts of Sexism using a Neural Framework

Parikh¹,

Abburi²,

Badjatiya³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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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 proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

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Section: Dataset Constructionmentioning

confidence: 99%

Multi-label Categorization of Accounts of Sexism using a Neural Framework

Parikh¹,

Abburi²,

Badjatiya³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

View full text Add to dashboard Cite

show abstract

“…Some saw the protests as a reflection of the legacy and contemporary strength of the women’s movement, while others declared this an “improper” feminism, indicated by the conservative impulse of calls for capital punishment for rapists, the protectionist rhetoric around women’s safety, and the selective concern for a metropolitan aspiring middle‐class young woman rather than, for example, a rural lower‐caste/class woman (e.g. Dutta and Sircar 2013; Tellis 2012). In the wake of these anti‐rape protests, many new initiatives to promote gender equality emerged, many of which were led by metropolitan youth.…”

Section: Contextmentioning

confidence: 99%