Search

The underlying relationship dynamics leading to authority gradients between frontline rail workers are being overlooked despite rail incident investigation reports pointing to the contrary. The aim of this study was to understand how the power disparities across multidisciplinary rail frontline workers can lead to authority gradients from the perspective of network controllers—the group at the apex of the operational hierarchy. A scenario generation technique was used to scaffold interviews with 55 rail network controllers from eight organisations across Australasia and identify how they perceived risky situations with all data thematically analysed. Authority gradient generation was found to be defined through: (1) motivations for network controllers to retain a position of authoritarianism toward train crew and track workers; and (2) behaviours that reinforced a power differential by curtailing their empowerment. Network controllers feared the probability and consequence of error and mistrusted in the capabilities of train crew and track workers, questioning their honesty, levels of competence, and believing they wanted to inherently cut corners. These motivations created a contemptuous regard that bred hostility and disparaging language, and engendered intimidation tactics where network controllers acted punitively and pressured train crew and track workers into compliance. The results from the scenarios point to perceptions of risk as the catalyst for fear and mistrust, with heightened perceptions of risk associated with increased vigilance and hostility across groups. This insight into how authority gradients are generated across operational teams in rail provides a new dimension to understanding teamwork error. [ABSTRACT FROM AUTHOR]

Modelling annual growth of individuals in a size-structured model requires calculation of the size-transition probabilities for moving from one size class to another. This requires evaluation of two-dimensional integrals when there is individual variability in growth. For computational simplicity, it is common to approximate the integrals by setting all individuals in a size class to the midsize of that class or by ignoring the individual variability. We develop a more accurate approximation that assumes a uniform distribution in size within each size class. The approximation is fast and hence feasible for Bayesian models in which the matrix of transition probabilities must be computed for each posterior sample. The improved accuracy of the new approximation is shown to hold over a diverse range of formulations for incremental growth. For the New Zealand Paua 5A (Haliotis iris) stock assessment model, it was found to reduce the average approximation error of the size-transition probabilities by 86% and 98% compared with the midpoint and deterministic growth approximations, respectively. Moreover, the midpoint and deterministic approximations inflated the estimated maximum sustainable yield by 6% and 8%, respectively, and the current biomass by almost 30% in comparison with the more accurate approximation. [ABSTRACT FROM AUTHOR]