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State-building in Post-independence Ireland and India
No full textThis thesis compares state formation in the early years of the Indian and Irish states. There are unusual similarities between the first and joint-second states to secede from the British Empire in the twentieth century considering the enormous geographical, political, and cultural gulf between them. Yet the many commonalities between the early Irish and Indian states are notable for the very reason of their widely differing contexts. There has been some academic focus on Irish-Indian connections and comparison in the years leading up to the independence of each, but the historiography mostly falls silent on this topic in the aftermath. This study addresses the following questions from the historian's perspective, though with due attention to the political theory of state-building: how did each state proceed to establish its institutions in the three key areas of 1) taxation, 2) law and order, and 3) the military? And what do the parallels and divergences of these processes signify about postcolonial state formation more broadly? This thesis finds significant analogy between the two states in the first decade or so of independence in all three areas. Twin themes that resurface throughout the work are continuity and centralisation. Institutional continuity between the Raj and independent India was the more readily apparent, but in Ireland too the norms and behaviours of the previous regime were remarkably resilient within new institutions. By contrast it was in the unitary Irish Free State that the centralisation of power in the executive was more extreme, but a similar process is also demonstrated in an Indian federation in which the centre dominated the constituent States - parallels that are striking in light of their vastly dissimilar contexts. The thesis also contributes to the broader study of state formation through what is derived concerning the value of macro historical studies in the comparative, what constitutes success in state-building, and importance of inertia as a factor. By focusing on state institutions rather than individual state-builders, this study depersonalises to a degree the issue of causality in the historiography of state formation in both cases
Clinical Outcomes of Anterior Cruciate Ligament Reconstruction in Gaelic Games Players
Get PDFGaelic games, specifically Gaelic football, hurling, and camogie, are high-intensity, level 1 field sports. However, there is a lack of data regarding the outcomes of anterior cruciate ligament reconstruction (ACLR) in Gaelic games player
Unsafe AI for Education: A Conversation on Stochastic Parrots and Other Learning Metaphors
Get PDFThis interview article discusses the impact on popular and educational discourses of the metaphor for a Large Language Model of a “stochastic parrot”. The metaphor comes from the title of an influential paper on the harms of large language models from 2021. Here we present a perspective on the short but influential history of the metaphor through an interview with one of its creators, Professor Emily M. Bender. Using the broad lens of metaphor as a way to shape and frame discourse, the editors interviewed Professor Bender and asked her a series of questions to spark discussion around AI in Education. A variety of topics were covered, including: on how metaphors and anthropomorphisation when carelessly used can elide harms and obviate responsibilities; the role of BigTech, data theft and metaphors of colonisation; Whether AI is unsafe for education and if so to which learners; Techno-solutionism; Positionality and AI “voice”; and whether EdTech is a key driver of AI bullshit and enshittification.This article aims to give readers an accessible insight into how a particular metaphor may be enacted in discourse and to contribute to wider critical debates about how GenAI operates in the context of datafication and educational harms
“Terrifying and brilliant...so many different kids, so many different stories”: Post-primary teachers’ perceptions and experiences relating to the inclusion of learners for whom English is not their first language in a DEIS and in a non-DEIS school: A dual case study.
No full textRecent Central Statistics Office (CSO, 2023) figures have shown that Ireland’s newcomer population is now over 757,000 constituting 14.3 per cent of the total population. A rise in newcomer families has resulted in Ireland’s post-primary schools now becoming increasingly
multicultural and multilingual educational spaces. As a result, post-primary teachers have been required to adapt their teaching pedagogies and methodologies to be more conscious and
inclusive of newcomer learners. This dual case study seeks to explore how post-primary EAL learners are construed, how they are valued, the processes that construct them and, how these processes operate within and across a DEIS and in a non-DEIS school? In choosing a DEIS and fee-paying non-DEIS school, this study purposefully explored, through qualitative methods, whether an economic and/or cultural resource disparity existed for teachers implementing language supports for newcomer learners. It also sought to examine how teachers in both settings implemented language supports to affect the inclusion of these learners. Gramscian and Bourdieusian theoretical frameworks contribute to this study’s critical exploration of cultural hegemony and reproduction, social and economic capital, and selfworth. Perceptions around social class, culture and identity especially in relation to the ‘migrant’ identity are also unpacked. Ainscow’s “ecology of equity” model provides a framework for discussion of study findings from the perspective of inclusive education. Findings and discussion suggest that school and teacher autonomy played a significant role in implementing initiatives for inclusion for students for whom English is an additional language.
Finally, the study highlighted how the Covid-19 pandemic exacerbated educational and social inequalities relating to learners in the schools studied. It is hoped that these findings will contribute to knowledge and practice bases relating to the construction of learners’ identities
for whom English is not their first language at post-primary level. It provides evidence of the implementation of school supports aimed at the inclusion of these learners and may have implications for the development of policy in this area going forward
Family-driven innovation: A multilevel investigation of contingency factors for innovation strategy
Get PDFThis paper explores the drivers of innovation in family firms. Using contingency theory as our theoretical lens, we investigate how contingency factors (‘where’, ‘how’, and ‘what’) relate to the development of innovation strategies, and how family firm idiosyncrasies affect the development of these innovation strategies. Using four multi-generational family firm case studies, the data collection consisted of 21 interviews and 1,496 items of archival data. We identify five specific elements of family firm innovation strategies (vision and culture alignment, generational approaches, change strategy, future orientation, and shared decision-making). We show how innovation strategy in family firms is contingent on three factors: ‘Where’: willingness to innovate; ‘How’: structures and processes needed to innovate; and ‘What’: capabilities and resources needed to innovate, and that these are influenced by idiosyncratic characteristics of family firms (familiness, founder influence, and succession)
Bayesian Hierarchical Risk Premium Modeling with Model Risk: Addressing Non-Differential Berkson Error
Get PDFFor general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios
Solutions for improved quality high bitrate multimedia delivery in MEC networks
No full textThe exponential growth of mobile data usage and video streaming has required advancements in network technology to meet the increasing demand for high-quality, real-time media delivery. Cloud computing has been proposed as a primary solution for processing and storing vast amounts of network-generated data. However, this approach requires significant data transmission, degrading network performance and challenging real-time application requirements. Multi-access Edge Computing has emerged to address these issues by bringing computing and storage resources closer to the data source and the Radio Access Network (RAN). MEC enables real-time data processing with lower delay and energy consumption than traditional cloud-based architectures.
Lately, the landscape of HTTP Adaptive Streaming (HAS) transformed significantly. Video content consumption surged in popularity, including Video on Demand, live streaming, video sharing, video conferencing, and applications incorporating video technologies like Augmented, Virtual and Extended Reality. By 2027, mobile video traffic is projected to constitute 79 percent of total mobile traffic, highlighting the need for efficient resource allocation in telecommunication networks. MEC offers a solution by storing video content at the network edge, reducing delivery delay and backhaul congestion while enhancing Quality of Service (QoS) and
Quality of Experience (QoE).
This research introduces novel solutions to optimize resource management in MEC-based networks. The The Multi-access Edge Computing-Optimization Problem-Server Allocation (MEC-OP-SA) algorithm proposes a cross-layer joint optimization model leveraging MEC and Server and Network Assisted Dynamic Adaptive Streaming
over HTTP (SAND-DASH) to balance QoE, fairness, and system utilization while addressing radio resource constraints. The Cross-Layer QoE-Driven Bitrate Allocation (CLQDBA) algorithm introduces a low-complexity, greedy-based method to improve system utilization, maximize QoE, and reduce backhaul traffic by caching popular videos. The MEC Collaborative Cross-Layer Bitrate Allocation (MCCBA) algorithm enhances QoE through collaboration between MEC servers and RAN components, addressing resource allocation challenges while improving fairness. Additionally, this study provides a comprehensive survey of current solutions, trends, and open issues in MEC and HAS research. These contributions are rigorously evaluated through simulations, confirming significant improvements in QoE, fairness, and system performance across diverse scenarios
Sample and time efficient strategies for off-policy reinforcement learning in robotic manipulation tasks
Get PDFIn recent years, reinforcement learning algorithms have become very popular in solving robotic manipulation tasks. However, these algorithms usually suffer from sample inefficiency, meaning that they require extensive exploration to find an appropriate control policy capable of solving the robotic manipulation tasks targeted, and, consequently, this sample inefficiency leads to training time inefficiency. The work presented in this thesis aims to solve this problem through three main methods: (i) reward engineering; (ii) a sequential execution of primitive skills; and (iii) an automatic execution of primitive policies. In particular, a novel reward that encourages lifting behaviour is presented and evaluated in the context of the Real Robot Challenge competition problem. A novel learning framework for in-hand object 3D rotations based on sequentially executed primitives is proposed and evaluated in the Shadow Dexterous Hand simulated environment of the OpenAI Gym. Finally, a new algorithm is proposed that is capable of automatically deciding, during exploration, whether or not to use a primitive behaviour, with this approach evaluated
in different tasks for different manipulators available in the OpenAI Gym
‘Don’t do anything special for us coming’: the mental health impact of Ofsted inspections on teacher educators in England
Get PDFOfsted inspections of Initial Teacher Education (ITE) providers aim
to enhance training quality for pre-service teachers in England.
However, research rarely examines the impact of these inspections
on the wellbeing of Teacher Educators (TEs) based in Higher
Education Institutions (HEIs). This study, part of a broader investigation into burnout among HEI-based TEs in Ireland and the UK,
focuses on the English context, where the inspection practices of
Ofsted have been identified as significant stressors. Drawing on
data from the Copenhagen Burnout Inventory (CBI), open-ended
survey questions and interviews, this study provides preliminary
insights into the mental health effects of Ofsted inspections on TEs.
It reveals that inspection processes contribute to anticipatory stress,
increased workload, and performative pressures, negatively impacting TEs’ professional morale and wellbeing. The paper recommends
reforms such as predictable inspection schedules, streamlined documentation, and dedicated mental health support for TEs during
the inspection period
From Programming to Prompting: Developing Computational Thinking through Large Language Model‑Based Generative Artificial Intelligence
Get PDFThe advancement of large language model-based generative artifcial intelligence (LLM-based GenAI) has sparked signifcant interest in its potential to address challenges in computational thinking (CT) education. CT, a critical problem-solving approach in the digital age, encompasses elements such as abstraction, iteration, and generalisation. However, its abstract nature often poses barriers to meaningful teaching and learning. This paper proposes a constructionist prompting framework
that leverages LLM-based GenAI to foster CT development through natural language programming and prompt engineering. By engaging learners in crafting and refning prompts, the framework aligns CT elements with fve prompting principles, enabling learners to apply and develop CT in contextual and organic ways. A three-phase workshop is proposed to integrate the framework into teacher education, equipping future teachers to support learners in developing CT through interactions
with LLM-based GenAI. The paper concludes by exploring the framework’s theoretical, practical, and social implications,
advocating for its implementation and validation