The Bachelor of Youth and Community Leadership (BYCL) was launched by the University of Canterbury (UC) in 2020. The genesis of this new degree was a Stage One service-learning course that, in turn, arose from the innovative and active response of many of the university’s students in the aftermath of the Christchurch earthquakes in 2010 and 2011. That innovative action saw the formation of the Student Volunteer Army as well as the adoption of a new set of Graduate Attributes for every undergraduate at the university. The idea of a specialist undergraduate degree that captured this unique chain of events began to take form from 2016. The resulting degree was developed as a flexible, transdisciplinary programme for young (and not so young) leaders wanting an academic grounding for their passions in community leadership and social action. In 2020, the inaugural intake of students commenced their studies. In this reflection, we discuss our experience of teaching within the BYCL for the first time, using a collaborative approach to teaching that we based on what we understand, individually and collectively, to draw on principles of democratic pedagogy.
High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination of whether a ground motion record is high-quality is poorly handled by automation with mathematical functions and can become prohibitive if done manually. Machine learning applications are well-suited to this problem, and a previous feed-forward neural network was developed (Bellagamba et al. 2019) to determine high-quality records from small crustal events in the Canterbury and Wellington regions for simulation validation. This prior work was however limited by the omission of moderate-to-large magnitude events and those from other tectonic environments, as well as a lack of explicit determination of the minimum usable frequency of the ground motion. To address these shortcomings, an updated neural network was developed to predict the quality of ground motion records for all magnitudes and all tectonic sources—active shallow crustal, subduction intraslab, and subduction interface—in New Zealand. The predictive performance of the previous feed-forward neural network was matched by the neural network in the domain of small crustal records, and this level of predictive performance is now extended to all source magnitudes and types in New Zealand making the neural network applicable to global ground motion databases. Furthermore, the neural network provides quality and minimum usable frequency predictions for each of the three orthogonal components of a record which may then be mapped into a binary quality decision or otherwise applied as desired. This framework provides flexibility for the end user to predict high-quality records with various acceptability thresholds allowing for this neural network to be used in a range of applications.
