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Among critical approaches to school textbook research, critical discourse studies (CDS) has proven itself a valuable and productive analytical approach. This paper surveys a database of 60 recent peer-reviewed research articles that use CDS approaches to investigate school textbooks. All the reviewed articles were published between 2018 and February 2024 and were drawn from various scholarly databases. The review aims to trace the regional coverage of the studies, types of the analyzed textbooks, the main used CDS frameworks and the major issues that were investigated. Findings indicate that oppressive discourses are the primary focus of most studies, with gender, sexuality, ethnicity, and minority groups, being particularly prominent. While English as a Foreign language (EFL) textbooks are prevalent among investigated textbooks, the regional coverage is diverse, an evidence of the growing popularity of CDS in textbook research. The review suggests that future research should investigate underexplored textbook types, expand its focus beyond well-researched topics to include under-studied emerging global issues and incorporate under-used CDS approaches to better capture the complexities of textbook discourse.

Developing countries are faced with social and economic challenges, including the emergence and proliferation of slums. Slum detection and localization methods typically rely on regular topographic surveys or on visual identification of high-resolution spatial satellite images, as well as socio-environmental surveys from land surveys and general population censuses. Yet, they consume so much time and effort. To overcome these problems, this paper exploits well-known seven pretrained models using transfer learning approaches such as MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50, consecutively, on a smaller dataset of medium-resolution satellite imagery. The accuracies obtained from these experiments, respectively, demonstrate that the top three pretrained models achieve 98.78%, 97.9%, and 97.56%. Besides, MobileNets have the smallest memory sizes of 9.1 Mo and the shortest latency of 17.01 s, which can be implemented as needed. The results show the good performance of the top three pretrained models to be used for detecting and localizing slum housing in northern Morocco.