The purpose of the thesis is to analyse the function fulfilled by the Youth Opportunities Programme (1978-1983) in its wider political, economic and historical context. There are three main sections to the work. The first establishes the context from which the Youth Opportunities Programme (YOP) emerged. This includes: an analysis of the origins and early development of the Manpower Services Commission (MSC); an historical account of government policy and special measures for the relief of unemployment; and a description of the circumstances and manner in which these elements came together in the development of the MSC's special measures policy, leading to the launch of YOP in April 1978. It is concluded that the initial role of the programme was essentially that of a palliative in the context of the Labour Government's social contract relationship with the trade union movement rather than being a positive element in the MSC's development of a comprehensive manpower policy. The second section is concerned with the actual development and performance of the programme in relation to its original objectives. This includes national level analyses in terms of both quantitative and qualitative objectives, and the conclusions of a case study conducted in the Portsmouth Travel-to-Work Area. The third section examines the significance of the divergences revealed between objectives and results, both in the context of contemporary political and economic developments, and also in a wider historical context which includes the initial progress made by YOP's successor, the Youth Training Scheme (YTS). It is concluded that, although YOP continued to act as a palliative, it developed beyond this towards a new form of active manpower policy consistent with a monetarist macro-economic context. On the basis of this analysis, alternative scenarios for the future are briefly considered.

In this paper, a study is conducted to investigate the use of Curriculum Learning as an approach to address accuracy issues in a neural network caused by training on a Long-Tailed dataset. The thesis problem is presented by a Swedish e-commerce company. Currently, they are using a neural network that has been modified by them using a CORAL framework. This adaptation means that instead of having a classic binary regression model, it is an ordinal regression model. The data used for training the model has a Long-Tail distribution, which leads to inaccuracies when predicting a price distribution for items that are part of the tail-end of the data. The current method applied to remedy this problem is Re-balancing in the form of down-sampling and up-sampling. A linear training scheme is introduced, increasing in increments of $10\%$ while applying Curriculum Learning. As a method for sorting the data in an appropriate way, inspiration is drawn from Knowledge Distillation, specifically the Teacher-Student model approach. The teacher models are trained as specialists on three different subsets, and furthermore, those models are used as a basis for sorting the data before training the student model. During the training of the student model, the Curriculum Learning approach is used. The results show that for Imbalance Ratio, Kullback-Liebler divergence, Class Balance, and the Gini Coefficient, the data is clearly less Long-Tailed after dividing the data into subsets. With the correct settings before training, there is also an improvement in the training speed of the student model compared to the base model. The accuracy for both the student model and the base model is comparable. There is a slight advantage for the base model when predicting items in the head part of the data, while the student model shows improvements for items that are between the head and the tail. / I denna uppsats genomförs en studie för att undersöka användningen av Curriculum Learning som en metod för att hantera noggrannhetsproblem i ett neuralt nätverk som är en konsekvens av träning på data som har en Long-Tail fördelning. Problemstälnningen som behandlas i uppsatsen är tillhandagiven av ett svensk e-handelsföretag. För närvarande använder de ett neuralt nätverk som har modifierats med hjälp av ett CORAL-ramverk. Denna anpassning innebär att det istället för att ha en klassisk binär regressionsmodell har en ordinal regressionsmodell. Datan som används för att träna modellen har en Long-Tail fördelning, vilket leder till problem vid prediktering av prisfördelning för diverse föremål som tillhör datans svans. Den nuvarande metod som används för att åtgärda detta problem är en Re-balancing i form av down-sampling och up-sampling. Ett linjärt träningschema introduceras, som ökar i steg om $10\%$ medan Curriculum Learning tillämpas. Metoden för att sortera datan på ett lämpligt sätt inspires av Knowledge-Distillation, mer specifikt lärar-elevmodell delen. Lärarmodellerna tränas som specialister på tre olika delmängder, och därefter används dessa modeller som grund för att sortera datan innan tränandet av elevmodellen. Under träningen av elevmodellen tillämpas Curriculum Learning. Resultaten visar att för Imbalance Ratio, Kullback-Libler-divergens, Class Balance och Gini-koefficienten är datat tydligt mindre Long-Tailed efter att datat delats in i delmängder. Med rätt inställningar innan tränandet finns även en förbättring i träningshastighet för elevmodellen jämfört med basmodellen. Noggrannheten för både elevmodellen och basmodellen är jämförbar. Det finns en liten fördel för basmodellen vid prediktering av föremål i huvuddelen av datan, medan elevmodellen visar förbättringar för föremål som ligger mellan huvuddelen och svansen.