Pioneering use of narrative method in a first-year law course to provide an authentic, inquiry-based entry into the discipline of law

Law is saturated with stories. People tell their stories to lawyers; lawyers tell their client's stories to courts; and legislators develop regulation to respond to their constituent's stories of injustice or inequality. My approach to first-year legal education respects this narrative tradition. Both my curriculum design and assessment scheme in the compulsory first-year subject Australian Legal System deploy narrative methodology as the central teaching and learning device. Throughout the course, students work on resolving the problems of four hypothetical clients. Like a murder mystery, pieces of the puzzle come together as students learn more about legal institutions and the texts they produce, the process of legal research, the analysis and interpretation of primary legal sources, the steps in legal problem-solving, the genre conventions of legal writing style, the practical skills and ethical dimensions of professional practice, and critical inquiry into the normative underpinnings and impacts of the law. The assessment scheme mirrors this design. In their portfolio-based assignment, for example, students devise their own client profile, research the client's legal position and prepare a memorandum of advice.

An extended regression approach to estimating loads and their uncertainties in Great Barrier Reef catchments

There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates by minimizing the biases and making use of possible predictive variables. The load estimation procedure can be summarized by the following four steps: - (i) output the flow rates at regular time intervals (e.g. 10 minutes) using a time series model that captures all the peak flows; - (ii) output the predicted flow rates as in (i) at the concentration sampling times, if the corresponding flow rates are not collected; - (iii) establish a predictive model for the concentration data, which incorporates all possible predictor variables and output the predicted concentrations at the regular time intervals as in (i), and; - (iv) obtain the sum of all the products of the predicted flow and the predicted concentration over the regular time intervals to represent an estimate of the load. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized regression (rating-curve) approach with additional predictors that capture unique features in the flow data, namely the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and cumulative discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. The model also has the capacity to accommodate autocorrelation in model errors which are the result of intensive sampling during floods. Incorporating this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach using the concentrations of total suspended sediment (TSS) and nitrogen oxide (NOx) and gauged flow data from the Burdekin River, a catchment delivering to the Great Barrier Reef. The sampling biases for NOx concentrations range from 2 to 10 times indicating severe biases. As we expect, the traditional average and extrapolation methods produce much higher estimates than those when bias in sampling is taken into account.