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The use of algorithms and automation of public services is not new, but in recent years there has been a step change in processing power and a decrease in the price of these technologies, which means we are seeing more widespread use. These advances are reframing our perception of what matters in ways that impact the ethical dimensions of day-to-day life. In turn, these changes challenge long-standing assumptions about public service ethics and how it is taught. In this multidisciplinary authored paper, we argue that public service leaders must be attentive to ethical questions that converge around adopting "data-driven" techniques, including algorithmic decision-making. Algorithmic and technology focused ethics question assumptions about the current deficits within public service ethics pedagogy in public service programs and university programs and the future direction of the discipline. To do so raises longstanding but neglected questions about the public services' role in the state and recovering what Rohr refers to as the 'ethics of the office.' This, we argue, will have implications for teaching public service ethics.

Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's "Human Rights and Technology final report," this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

In the recent Australian 2021 census, the socio-technical construct of algorithmically driven decision-making processes made LGBTQI+ data as a category of diversity, inclusion and belonging an absent presence. In this paper, we position the notion of 'data justice' in relation to the entrenchment of inequalities and exclusion of LGBTQI+ lives and consider the implications of LGBTQI+ data being missing in action. As we look at the notion of 'data justice', we consider five critical socio-technical imaginaries with different kinds of data to think through the implications of technical democracy, data justice and post-automation. Finally, we consider the imaginary of citizenship when LGBTQI+ data is habitually missing in action from systematic power integrated into forms of governance. This paper positions 'data' not as a static 'object or process' but as a dynamic ecology that carries with it a multi-faceted set of coded meanings requiring constant review and reconsideration.

The Chameleon Educational Policy Reforms' (CEPR) 25th anniversary was celebrated at the Global General Assembly with the 2075 Decennial Analysis of Schooling (DecAS) announcement of the attainment of a benefit-cost ratio of >1.0. The attainment of a global positive net value of education is directly linked to the educational reforms established to 'provide the right access to the right education for all people' (CEPR, 2050). The Chameleon reforms, informed by The Algorithm, produce policies that instantly adapt to the learning environment and needs of students. Barriers that effect students' learning are removed, in stark contrast to historic processes that viewed students as the barrier to be removed from learning environments. The case studies presented in this paper are guided by three questions: 'Where are we now?', 'How did we get here?', and 'Are we there yet?'. Questioning the 'here and now', directs a look back from educational engagement currently governed by the CEPR to key moments and movements in the attainment of past grand policy announcements that no child be left behind to live in poverty. And 'yet', leads to critical consideration of the ongoing engagement with The Algorithm. Artifacts detailing the 2045 commencement of annual donations from the richest two percent to fully fund global education remain sealed. The results of the annual donations are, however, publicly available; and some would say exploited (Gerve, 2072). Achieving self-sustainable economic cost-benefit status for the CEPR may release the Donators from those agreements and untether society from the Donators.

To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively. [ABSTRACT FROM AUTHOR]

The equivalent source technique is a powerful and widely used method for processing gravity and magnetic data. Nevertheless, its major drawback is the large computational cost in terms of processing time and computer memory. We present two techniques for reducing the computational cost of equivalent source processing: block-averaging source locations and the gradient-boosted equivalent source algorithm. Through block-averaging, we reduce the number of source coefficients that must be estimated while retaining the minimum desired resolution in the final processed data. With the gradient-boosting method, we estimate the sources coefficients in small batches along overlapping windows, allowing us to reduce the computer memory requirements arbitrarily to conform to the constraints of the available hardware. We show that the combination of block-averaging and gradient-boosted equivalent sources is capable of producing accurate interpolations through tests against synthetic data. Moreover, we demonstrate the feasibility of our method by gridding a gravity data set covering Australia with over 1.7 million observations using a modest personal computer. [ABSTRACT FROM AUTHOR]

There is a growing concern around the dependency of news organisations on platforms like Facebook for audience traffic. However, scholars are still working out the extent of this dependency and how it manifests in practice. In this article, we draw on interviews with Australian news professionals and industry fieldwork to provide a nuanced account of this phenomenon. We find that news media organisations have recently started to diversify their distribution strategies and the business models associated with them in response to Facebook's algorithm changes. While Facebook remains important, we suggest that greater attention needs to be paid to the complex relationships that news organisations have with platforms. This article ends by considering the implications of these findings for international policy discussions centred around the prospect of platform regulation. [ABSTRACT FROM AUTHOR]