In the 21st century, talent is dynamic, with workplaces being defined by diverse sexual orientations. In this context, a quantitative approach through the lens of a bibliometric technique of citation and co‐citation analyses was applied to study 456 publications on the topic of the transgender workforce from 1988 to 2022, and a co‐word analysis was used to showcase a visual representation of the concept. This research unravels significant lines of output; for instance, it assessed the publication efficiency of authors, journals, (...) institutions, and nations. We obtained three clusters, which are: multilayered workplace discrimination against transgender employees, policies and practices to support transgender employees in the workplace, and the multi‐level impact of negative workplace experiences on transgender employees. Given the volume of literature, it is difficult for bibliometric analyses to unearth the entire knowledge structure of the concept. Therefore, this study attempts to understand the knowledge structure of the transgender workforce from a quantitative perspective. In addition, this study draws from and integrates disciplines like sociology, management, and psychology. Lastly, this research discusses how the concept has evolved over the decade. (shrink)

Bloom provides a detailed account of children's word learning and comprehension. Yet, this book falls short of explaining the developmental process of word learning. The studies reviewed do not explain how infants begin to map words onto objects or the environment's facilitative role. Researchers must describe how several factors interact and explain the relative importance of each during the development of word learning.

Locke & Bogin (L&B) suggest that theoretical principles of ontogenetic development apply to language evolution. If this is the case, then evolutionary theory should utilize epigenetic theories of development to theorize, model, and elucidate the evolution of language wherever possible. In this commentary, I evoke principles of dynamic systems theory to evaluate the evolutionary phenomena presented in the target article.

Millions of memes are created and shared every day on social media platforms. Memes are a great tool to spread humour. However, some people use it to target an individual or a group generating offensive content in a polite and sarcastic way. Lack of moderation of such memes spreads hatred and can lead to depression like psychological conditions. Many successful studies related to analysis of language such as sentiment analysis and analysis of images such as image classification have been performed. (...) However, most of these studies rely only upon either one of these components. As classifying meme is one problem which cannot be solved by relying upon only any one of these aspects, the present work identifies, addresses, and ensembles both the aspects for analyzing such data. In this research, we propose a solution to the problems in which the classification depends on more than one model. This paper proposes two different approaches to solve the problem of identifying hate memes. The first approach uses sentiment analysis based on image captioning and text written on the meme. The second approach is to combine features from different modalities. These approaches utilize a combination of glove, encoder-decoder, and OCR with Adamax optimizer deep learning algorithms. Facebook Challenge Hateful Meme Dataset is utilized which contains approximately 8500 meme images. Both the approaches are implemented on the live challenge competition by Facebook and predicted quite acceptable results. Both approaches are tested on the validation dataset, and results are found to be promising for both models. (shrink)