Sexism has pervasive negative effects on both individuals and society. This paper presents a generalizable BERT-based approach to identifying and classifying the source intent of sexism across different social network channels. This approach focuses on individual models trained on the text of tweets and then applied to both Meme (image) and Video data using OCR and annotations respectively. The identification model performed well across all channels and the classification model performed well on both Tweets and Memes. This research suggests that a single model, fine-tuned on one media type can be effectively applied to multiple media types with minimal data preprocessing required.

Accurate Species Distribution Modelling (SDM) is essential for biodiversity conservation, however the limited and spatially biased nature of Presence-Absence (PA) data poses a challenge. In contrast, Presence-Only (PO) datasets are abundant but lack explicit absence records. This paper examines a two step deep learning approach to combining both PO and PA data to generate an SDM. In the first step, the model was trained on a larger PO dataset, and in the second the model was then tuned on a smaller PA dataset. Results indicate that pre-training with PO data improved the performance by 7% when subsequently fine-tuned with PA data, as measured by the samples-averaged F1-score. This approach demonstrates the potential of combining diverse data types to create more reliable species distribution models for plant biodiversity conservation.