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The fragmentation of academic work and its uneven distribution among academic staff have produced particular challenges for new entrants to teaching in Higher Education, Early Career Teachers [ECTs]. In this paper, documentary analysis of the narratives of fourteen ECTs, who worked across six different continents, was undertaken. The findings highlight the diverse forms of precarity that ECTs face, which cut across migratory, identitarian, economic, and ideological dimensions. It discusses ECTs' reflections on their expectations of teaching and their adaptation to the demands of neoliberal Higher Education. Drawing from their narratives and Sevil Sümer's theories of differentiated academic citizenship, ECTs are recognised as 'precarious academic citizens'. This has important implications for revealing the unique circumstances of this group, thereby opening further questions as regards their mentoring and support to enable them to be situated more equally as citizens of the academy.

Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain. • Bayesian methods for modelling diffusion curves with radiocarbon dates are introduced. • Sample interdependence and non-sigmoid diffusion curves can be handled. • Robustness is tested on simulated datasets and archaeological case studies. • Code and data are available on a GitHub repository and archived on Zenodo. [ABSTRACT FROM AUTHOR]