Neo-liberalism, Human Capital Theory and the Right to Education: Economic Interpretation of the Purpose of Education

With the end of WW II, a new world order emerged that recognised the significance of human rights as part of the remedial measures to institute global peace. This is recognised in Articles 1(3), 13(1)(b) and 55(c) of the 1945 United Nations Charter. Thereafter, the human rights ideals recognised by the UN Charter were codified into the Universal Declaration of Human Rights (UDHR) 1948 (Fait, 2015: 26). Despite not having a binding force, the UDHR became a standard-setting instrument covering all generations of human rights including the right to education. Later, two distinct treaties – i.e., the International Covenant on Civil and Political Rights (ICCPR) 1966 and the International Covenant on Economic, Social and Cultural Rights (ICESCR) 1966 were adopted as a follow-up to the UDHR. Articles 13 and 14 of the ICESCR made more expansive provisions on the right to education than Article 26 of the UDHR. However, the adoption of policies driven by neoliberal ideals and associated neo-classical economic principles in the delivery of education has brought education under market forces, encapsulating it with an economic purpose. This makes education central to the realisation of the neoliberal ideology as schools focus on teaching technical skills and knowledge necessary for the achievement of the economic purposes of education. This paper argues that while the economic purpose of education which is in line with neoliberal and associated neo-classical economic principles is germane for states’ economic development, a holistic approach is consistent with the human rights purpose of education

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions

Unincorporated Associations and the Property Problem: The Contract-Holding Theory as the Ace of Clubs?

This article provides an examination of the contract-holding theory and reveals its flaws, proving it to be a misnomer. Sustained analysis also shows this method has much in common with other, supposedly distinct, property-holding methods, and that a contract is neither a necessary nor sufficient condition for an unincorporated association

Adaptation of Courts to Disruption

This article reflects on how courts in the USA and UK have remained active and resilient to provide access to justice, or due process, during times of emergency and disruptive events.. The focus here is not to define emergencies per se, but to analyse the impact of the emergencies and disruptive events that interrupt the functioning of courts and access to justice. The article provides a brief examination of the emergencies and the disruptions and the expected responses to those interruptions. The question for this paper is how do courts, adapt (or be adapted) in times of emergencies that disrupt their ordinary operation, both in terms of continuity of operations, but also in terms of protection of rights through judicial review? This paper will examine mainly two common law examples (England and USA) of how the courts adapted to such disruptions

The Theoretical Relevance of the Capabilities Approach in Discussing the Purpose of Education

This paper endeavours to use the Capabilities Approach (CA) to clarify the understanding of human dignity as the human right purpose of education and argue that access to education needs to be compulsory for all children in consonance with international human rights law (IHRL) and because of the significance of education in developing those capabilities needed to guarantee a life with dignity, which limits the discretion of states in accordance with the rules of IHRL. It argues that the CA is a relevant theoretical approach because it conceptualises valuable personal outcomes an individual could achieve through education i.e., the purpose education should achieve in the life of learners. This is an aspect that both IHRL scholars and the CA have not explored, i.e. investigating the purpose of education under IHRL and the insights and illuminations the propositions of the CA provide. As such, this paper contends that the CA emphasises human agency (what valuable things people can achieve) instead of markets and economic purposes. It endeavours to argue that the CA further accentuates the significance of access to schooling, which, in combination with the central function of human dignity in IHRL, allows the understanding of dignity as the fundamental purpose of education. This means that the CA contributes to a clearer and richer understanding of the use of human dignity in IHRL. It starts with the foundational work of Sen that is rooted in welfare economics, and more reliance is placed on Nussbaum’s philosophical work while discussing human dignity under the CA. This is because Nussbaum draws from moral and political philosophy in her version of the CA, which enhances our understanding of the theoretical conceptions of human dignity and its use under IHRL

ENAS-B: Combining ENAS with Bayesian Optimisation for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification from Ultrasound Images

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimises cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimisation in two folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimisation to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1,522 breast lesion ultrasound images is used for the searching and modelling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) showed that the proposed framework generates robust and light CNN models

Helping in Times of Crisis: Examining the Social Identity and Wellbeing Impacts of Volunteering During COVID-19

COVID-19 produced the largest mass mobilisation of collective helping in a generation. Currently, the impact of this voluntary activity is not well understood, particularly for specific groups of volunteers (e.g., new vs existing), and for different amounts of voluntary activity. Drawing on social psychological work on collective helping, and work from the Social Identity Approach to Health, we seek to address this gap through an analysis of survey data from 1001 adults living in the South of England (333 men; 646 women; Age range = 16–85) during the first UK lockdown. Measures included time spent volunteering pre/post COVID, community identification, subjective wellbeing, and volunteering intentions. Those who volunteered during COVID-19 reported higher levels of community identification than those who did not. However, subjective wellbeing benefits were only found for those volunteers who maintained the same level (in terms of time) volunteering pre-and-post the COVID lockdown. New volunteers showed significantly lower levels of wellbeing where they were undertaking 5 or more hours of volunteering a week. Our findings provide unique insight into the variable relationship with wellbeing for different groups of volunteers, as well as how the experiences and functioning of 'crisis’ volunteering is different to volunteering during ‘normal’ times

Exploring the Motivation of the United Kingdom’s Domestic Extremist Informants

ABSTRACT Understanding a potential informant’s motivation can lay the foundation for managing the risks and opportunities associated with the informant-handler relationship and operational deployments. The present research explored the self-disclosed and handler-assessed motivations of U.K. informants authorized to report against domestic extremists. Informants reported being motivated overwhelmingly by both ideological and financial considerations. Those reporting on right-wing domestic extremism primarily reported for financial reasons, while those reporting on left-wing extremism did so primarily for ideological reasons. The findings also revealed that motivation is neither one dimensional nor unchangeable, with most informants declaring financial and ideological reasons for informing. Handlers were accurate at identifying informants’ primary motivation, with a minority of the handler assessments revealing a perceived change after a six-month period. By designing recruitment approaches around ideological and financial motivational hooks, law enforcement and intelligence agencies may increase the probability of recruitment success, as well as enhance both the effectiveness and longevity of their informant-handler relationship

Vehicle Pair Activity Classification Using QTC and Long Short Term Memory Neural Network

The automated recognition of vehicle interaction is crucial for self-driving, collision avoidance and security surveillance applications. In this paper, we present a novel Long-Short Term Memory Neural Network (LSTM) based method for vehicle trajectory classification. We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between a pair of vehicles. The spatio-temporal features of the interacting vehicles are captured as a sequence of QTC states and then encoded using one hot vector representation. Then, we develop an LSTM network to classify QTC trajectories that represent vehicle pairwise activities. Most of the high performing LSTM models are manually designed and require expertise in hyperparameter configuration. We adapt Bayesian Optimisation method to find an optimal LSTM architecture for classifying QTC trajectories of vehicle interaction. We evaluated our method on three different datasets comprising 7257 trajectories of 9 unique vehicle activities in different traffic scenarios. We demonstrate that our proposed method outperforms the state-of-the-art techniques. Further, we evaluated our approach with a combined dataset of the three datasets and achieved an error rate of no more than 1.79%. Though, our work mainly focuses on vehicle trajectories, the proposed method is generic and can be used on pairwise analysis of other interacting objects

From the Wings to the Stage and Beyond; Performance Anxiety and Flow in UK Vocational Dance Students

Professional dancers have described high levels of performance anxiety while also experiencing flow on stage. However, such research tends to capture one period of time in the performance experience and rarely focuses on vocational dance students. The current study samples vocational dance students at a UK performing arts school and captures their cognitive, somatic and emotional experiences from pre- to post-performance. Eleven interviews were conducted with female students aged between 15 and 17 years. Thematic analysis was employed and three themes identified: Facilitative and Debilitative Anxiety in the Wings, Constructions of Anxiety and Flow on Stage, and After the Show; the Highs and the Lows. Findings produced an understanding of the psychological journey from pre- to post-performance. Students have the potential to manipulate their cognitions to facilitate flow suggesting that dance schools can implement psychological techniques to manage anxiety and increase flow, thus enhancing well-being and performanc