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The concept of 'powerful knowledge' has become extremely influential in discussions about curriculum in England over the last ten years. However, the concept seems to have done little to revolutionise curriculum design, and in some cases it has led to curricular narrowing and a focus on an increasingly nationalistic narrative in history. Michael Young (2019, 2021) has argued that the failure of the concept of 'powerful knowledge' to underpin meaningful curriculum reforms has been mainly due to its misinterpretation and loose definition. This paper explores these claims and finds that key voices in education in England, and history education specifically, have misunderstood and misapplied the concept of powerful knowledge. However, it also makes the case that powerful knowledge cannot be meaningfully defined in terms of history education, and that attempts to make curricular decisions based on the concept are therefore a distraction from more meaningful curricular work.

Aware both that causation is the bread and butter of the historian's craft, and that trainee teachers find it far harder to teach well than they anticipate, Alex Ford sought to get to the heart of the problem with causation, especially at GCSE. When teaching to a specification and mark scheme, both of which reward poorly substantiated causal thinking, there is a disincentive to do causation well. Ford got to the heart of the problem by doing a lot of reading -- both of pedagogy and of historical scholarship -- and came to the conclusion that causal thinking is enhanced by an association with a narrative of change, and that this can and should be delivered at GCSE too. This article charts his thinking, the lessons he designed to test his ideas, and the reaction of pupils and his trainee teachers.

When planning a GCSE period study on the American West, Alex Ford wrestled with reconciling the content demands of the examination specifications with the need to provide his students with a memorable narrative. In this article, Ford shows how he drew on the latest academic scholarship to construct a rigorous, coherent narrative outlining the competing 'visions' in the West. This narrative coherence then underpinned his students' sophisticated causal analyses.

Alex Ford and Richard Kennett both welcome the renewed emphasis on knowledge within recent curriculum reforms in England, but are concerned about some of the ways in which the principle of a 'knowledge-rich' curriculum has been interpreted and transformed into particular pedagogical prescriptions. In this article they explain their reasons for endorsing a renewed emphasis on subject knowledge before setting out the implications that arise from such a commitment to the development of rich subject knowledge. Drawing on examples from their own (recent and current) practice as history teachers and on an extended metaphor of the history teacher as 'conductor', they explain how careful preparation--in terms of the teacher's subject knowledge--and attention to the way in which knowledge needs to be structured and brought to life for young people will enable a history teacher to orchestrate a rich symphony of historical learning that is both enjoyable and empowering.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce—compared to other medical imaging data—we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data. [ABSTRACT FROM AUTHOR]

Online citizen science projects such as GalaxyZoo.sup.1, Eyewire.sup.2 and Phylo.sup.3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA.sup.4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit.sup.5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now--starting from an extended polypeptide chain--generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds--including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem. Proteins designed de novo by players of the online protein-folding game Foldit can be expressed in Escherichia coli and adopt the designed structure in solution., Author(s): Brian Koepnick [sup.1] [sup.2] , Jeff Flatten [sup.3] , Tamir Husain [sup.3] , Alex Ford [sup.1] [sup.2] , Daniel-Adriano Silva [sup.1] [sup.2] , Matthew J. Bick [sup.1] [sup.2] , [...]

Alex Ford was thrilled by the prospect of freedom offered to history departments in England by the abolition of level descriptions within the National Curriculum. After analysing the range of competing purposes that the level descriptions were previously forced to serve, Ford argues that the three distinct tasks of measuring current attainment, assessing the rate of students' progress and providing guidance to them about the next steps forward simply cannot be accomplished with reference to a single scale. Drawing on range of historical scholarship as well as on educational research and the practice of history teachers in other contexts, his department have developed their own conception of progression, inspired by the principles of serving an apprenticeship within the historian's craft. Ford shares the early stages of this development process, illustrating both the kind of assessment tasks and the tracking systems that he and his colleagues have developed. In outlining their exploratory work and the principles on which it is based, his hope is that it will inspire similar creativity and courage among others in resisting simplistic systems that fail to acknowledge either the nature of the subject or the range of purposes that any assessment and reporting system must serve.