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Apprenticeship by another name? Media discourses and the vocational turn in Nigerian higher education
Get PDFPurposeAs the UK marks a decade since the introduction of degree apprenticeships, sparking international interest in work-integrated models of Higher Education (HE), this paper shifts focus to Nigeria, a former British colony where global pressures, national priorities, and inherited academic traditions converge. Situated within a postcolonial context, Nigeria continues to grapple with British-influenced educational ideals while increasingly seeking to transform its HE system in response to youth unemployment and global competitiveness.Design/methodology/approachThis study investigates how HE is constructed in public discourse through a media analysis of three Nigerian news media, Punch, Daily Trust, and Daily Post, between 2021 and 2022, following the introduction of the Core Curriculum and Minimum Academic Standards (CCMAS) policy. The study combines thematic and discourse-historical approaches to analyse dominant media narratives and identify which voices influence public debates. Bourdieu's capital and institutional habitus logic guide the interpretation of how power and ideology shape media representations of HE.FindingsThe analysis identifies two main framings: HE as a pipeline for producing future workers and as a site increasingly focused on vocational, apprenticeship, and entrepreneurial training. Also, there is a hegemonic construction of HE in Nigeria, where academics and the government have prominent voices in the media. The findings visibilised the need for more inclusive conversations about HE's role, involving a wider range of stakeholders.Originality/valueThis paper adds a Southern perspective to international debates on HE reform, showing how apprenticeship-like models, though not formally institutionalised, are discursively promoted through media narratives advocating for employability, entrepreneurship, and work-readiness in Nigerian HE
Technology Emergence as a Structuring Process:A Complexity Theory Perspective on Blockchain
Get PDFDrawing on complexity theory, we investigate the structuring processes and underlying mechanisms underpinning the emergence of a new technology. Empirically, we track the emergence of blockchain technology by examining international patents issued between 2009 and 2020. Our results indicate that technology emergence follows an evolutionary trajectory that progresses from disordered to structured interactions among the technological elements, culminating in the formation of a technological core that acts as a pole of attraction for further interactions and delineates boundaries within the technological domain. Technology structuring is fueled by what we term βtechnology fitnessβ and βself-reinforcingβ mechanisms that progressively transform primitive structures into more complex, self-organized configurations. Our study offers a novel framework of technology emergence, highlighting how dispersed bits of technological knowledge gradually aggregate into complex structures that define the specific trajectory of a particular domain
An interactive technology-based emotion recognition intervention for children with developmental language disorder:a longitudinal mixed-method study
Get PDFRecent studies have shown that children with developmental language disorder (DLD) often face challenges in emotion recognition (ER) skills, which impacts their social and behavioural outcomes. This study aims to evaluate the effectiveness of a flexible online intervention in meeting the individual needs of children with DLD, focusing on ER skills. The study also sought to explore the impact of the ER intervention on the emotional well-being of children with DLD. A longitudinal mixed-method design was conducted with 10 children with DLD, employing a weekly assessment and comprehensive pre- and post-assessments to pinpoint specific ER challenges and changes. Weekly phone calls (WPC) were also used to follow-up on participantsβ progress and understanding of changes in ER skills. Individual analysis revealed that varied use of intervention phases and degrees of improvement were identified as families were flexible in this program. The intervention results highlighted significant improvements in basic vocal ER skills and emotional problems among children with DLD. Clinical implications include integrating targeted ER interventions into DLD intervention programs and identifying specific areas of difficulty for individualised support
Cryptocurrencies: Asset Classification, Trading and Portfolio Management
Get PDFCryptocurrenciesβ (cryptos) asset classification presents a conundrum: Cryptos classified as commodities require less regulation, promoting market participation. However, the uncertainties in volatility and correlation generate financial instability. Major cryptos (Bitcoin and Ethereum) exhibit similarities to commodities in terms of statistical features and non-linear machine-learning forecasting results. Trading strategies applied to a crypto and commodity asset pool demonstrate (1) momentum works effectively in bearish markets with high volatility (March 2020), suggesting investors herd in market downturns, and (2) pairs trading works well in high-interest, low-volatility environments (March-October 2023). The crypto futures-commodities portfolio has the highest Sharpe ratio, followed by the crypto spots-commodities portfolio, and lastly, the commodities-only portfolio. Heavy-tailedness violates modern portfolio theoryβs normality assumptions
Investigating the Impact of Deformable, Movable, and Rigid Surfaces on Force-Input Interactions
Get PDFThe force modality fundamentally transforms the interaction space of traditional touch input. When paired with compliant devices, which deform under force and provide immediate haptic feedback, there is potential to enhance user interactions significantly. However, the effects of compliance on force-input remain under-explored, with limited understanding of their full potential. This article presents the first systematic investigation of the impact of deformable, movable, and rigid surfaces on user performance and experience through three rigorous studies (each N = 28). The results reveal previously unreported effects, including (1) higher maximum comfortable forces on deformable surfaces, (2) user preference for soft and deformable surfaces over rigid surfaces, and (3) improved ability to maintain force input on softer surfaces. These results highlight the benefits of compliant surfaces, contrasting with the dominant use of force-input on rigid devices. These findings guide researchers and designers in optimizing user experience and performance of force-input interactions.</p
State pension eligibility age and retirement behaviour:evidence from the United Kingdom household longitudinal study
Get PDFWe examine individualsβ retirement behaviour in response to changes in the State Pension eligibility age (SPe-age) introduced in UK Pension Acts. Our findings show that the annual probability of retirement reduced significantly in response to a one-year increase in SPe-age, by 8.2pp and 6.4pp for men and women, respectively. They also show that younger individuals can adjust their Expected Age of Retirement (EAR) downwards in response to an increase in their SPe-age. Thus, while an increase in the SPe-age induces individuals to postpone actual retirement, it does not necessarily lead to certain groups of individuals to revise their EAR upwards, which could result in suboptimal retirement planning. The latter can be problematic for those with low occupational pension wealth and/or individuals who rely disproportionately on State Pension. Our findings suggest the need for targeted communication campaigns aimed at specific groups of prime aged workers to improve their retirement planning
Deep predictive coding with bi-directional propagation for classification and reconstruction
Get PDFPredictive Coding (PC) has emerged as a prominent theory underlying information processing in the brain. The general concept for learning in PC is that each layer learns to predict the activities of neurons in the previous layer, which enables local computation of error as well as in-parallel learning across layers. Deep Bi-directional Predictive Coding (DBPC) is proposed here as a new learning algorithm that enables neural networks to simultaneously perform classification and reconstruction tasks using the same learned weights. Building on existing PC approaches, DBPC supports both feedforward and feedback propagation of information. Each layer in the network trained using DBPC learns to predict the activities of neurons in the previous and next layers, enabling the network to simultaneously perform classification and reconstruction tasks using feedforward and feedback propagation, respectively. DBPC also relies on locally available information for learning, thus enabling in-parallel learning across all layers in the network. DBPC enables the training of both fully connected networks and convolutional neural networks. The classification accuracies of DBPC on the MNIST, Fashion-MNIST, and CIFAR-10 datasets (99.58%, 92.42%, and 74.29%, respectively) exceed those of well-established PC-based benchmark approaches (including FIPC3 and iPC) and are competitive with state-of-the-art Error-Backpropagation-based methods (including ResNet and DenseNet) on MNIST, Fashion-MNIST, and EuroSAT datasets. Importantly, DBPC achieves these results using significantly smaller networks for MNIST, Fashion-MNIST, and CIFAR-10 datasets (0.425, 1.004, and 1.109 million parameters), and every representation estimated in DBPC can be used for the reconstruction of inputs. The significant benefit of DBPC is its ability to achieve this performance using locally available information and in-parallel learning mechanisms, which results in an efficient training protocol. Overall, we demonstrate that DBPC is a much more efficient approach for training networks that can perform both classification and reconstruction simultaneously
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