‘Slappers like you don’t belong in this school’: the educational inclusion/exclusion of pregnant schoolgirls

Policy in England identifies pregnant schoolgirls as a particularly vulnerable group and emphasises the importance of education as a way of improving the life chances of those who become pregnant while young. This paper draws on repeat interviews conducted over a twelve-month period to compare and contrast the stories of four young women. The narratives show that despite a common policy framework, there is great variability between schools in staff attitudes towards and responses to pupil pregnancy which produce different accommodations and support for pregnant girls, and seem likely to produce very different outcomes. We mobilise Iris Marion Young’s five faces of oppression to conduct a second reading of the stories. This situates the specificity of the girls’ school experiences into a wider socio-cultural and economic framing and indicates what might be involved in actually initiating and implementing the kinds of changes that the first ‘face value’ reading suggests are necessary

PHOTONAI—A Python API for rapid machine learning model development

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com