Oxford Events, the new replacement for OxTalks, will launch on 16th March. From now until the launch of Oxford Events, new events cannot be published or edited on OxTalks while all existing records are migrated to the new platform. The existing OxTalks site will remain available to view during this period.
From 16th, Oxford Events will launch on a new website: events.ox.ac.uk, and event submissions will resume. You will need a Halo login to submit events. Full details are available on the Staff Gateway.
Join our next insaka on Friday 6th February, 2026 at 5:30pm UK time.
The insaka will be live-streamed on YouTube and we encourage you send in your questions via the YouTube chat box.
Speakers for this insaka are:
Global Herstoriography: an African perspective through the lens of plants
How can the knowledge of plants held by indigenous communities help us to rethink the role of African women as knowledge producers? How can these forms of deep knowledge help us address crises such as climate change? An example of long-survived feminist indigenous knowledge is the Khoe ‘Ausi’ intergenerational ecological and medicinal knowledge around the wetlands on the Cape Flats in the Western Cape in South Africa. During the colonial period of the Cape of Good Hope, ‘Ausi’ knowledge holders were superficially described as the ‘kruidvrou’ (woman of herbs) in early European travelers’ accounts. The matrifocal globally interconnected and metaphorical ‘Ausi’ knowledge of environmental sustainability and medicinal practices is an illustration of Africa’s deep-time interconnectedness. In this presentation I examine how recent feminist and decolonial approaches to the study of Africa offer us an innovative opportunity to reclaim these marginalized forms of knowledge. To do this we must address the limitations of Eurocentric approaches to knowledge production and research methodologies about Africa’s deep pasts.
Predicting Soil Erosion Risk and Sustainable Land Use in Tanzania’s Uluguru Mountains Using AI and Climate Geospatial Data
AI has the potential ability to understanding environmental change by helping us analyse large datasets and detecting patterns. In this paper, I focus on how artificial intelligence, satellite imagery, and climate data can be combined to predict soil erosion in Tanzania’s Uluguru Mountains a vital region for smallholder spice farming. These landscapes are increasingly at risk due to deforestation, poor land use decisions, and climate stress. By using deep learning techniques like CNN and LSTM, the project aims to develop an interactive GIS tool to offer guidance for sustainbale land management.
