Pupil resistance to their schooling experience

The thesis explores the nature of pupil resistance; it investigates what constitutes it and how it can be explained. Various ethnic and national group, male and female working-class resistance if analysed in two secondary schools in Birmingham (England) and one school in Sydney (Australia). It focuses on the pupils’ experience of school. ‘Compressed ethnographies’ (Walford and Miller, 1991) were conducted in each school to examine pupil resistance. The research found that structural societal state factors, regional, community and formal, informal and physical characteristics of each school, together with the teachers and pupils characteristics and background all influence resistance. The class, gender, ethnic and national identity of each pupil shapes resistance. In all three schools that were involved with the research, girls were more likely to exhibit overt, collective forms of resistance, whereas lads were more likely to operate alone. Islander pupils in Sydney and African-Caribbean kids in Birmingham were more likely to display engaged forms of resistance. Girls tended to show more engaged forms compared to their male counterparts across all ethnic and national cultures. Resistance is complex and dynamic, the definition alters depending upon context. Dimensions of resistance are developed; including overt, covert; individual, collective; intentional, unintentional; engaged and detached forms. Resistance operates within a structure and agency framework, the pupils can shape their own schooling experience mediated within the structures of their school, community and society. Some pupils manage their resources and the structures better than others; how the pupil manages and operates within the structures influences their resistance response. Resistance is contradictory and can reinforce the status quo. To fully understand resistance, it must be contextualised.

Education and racism:a study of the teachers' and the pupils' relations in the schooling of black boys

This thesis examines the teachers' and the pupils' relations in the schooling of black boys. The study using the methodology of participant observation focusses on one school (Kilby) in an area of black population in an English city. The thesis’s intentions are two fold: firstly, in order to examine these relations, two major aspects of their interaction are addressed, that of the absence of teachers from conventional 'race-relations' research, and, the identification and examination of the anti-school pupils' sub-cultures. Two substantive questions are asked: what is the response of the teachers to the schooling of black pupils? and, what is the meaning of the pupils' resistance to schooling? Secondly, in attempting to answer these questions and offer a critique of the dominant 'race-relations' culturalist explanation of black youth's response to schooling, a theoretical framework has been developed which takes account of both the 'economic' and the 'sociological' perspectives. Methodology allowed and pointed to the importance of examining the teachers' ideologies and practices as well as those of the black boys. It is argued that a class analysis of the racially structured British society is more adequate than the conventional ethnic approach in explaining the black boys' location within Kilby School. Hence, it is posited that the major problem in the schooling of black youth is not that of their culture but of racism, which pervasively structures the social reality at Kilby school. Racism is mediated both through the existing institutional framework that discriminates against working-class youth and through the operation of race specific mechanisms, such as the process of racist stereotyping. It is thus further argued that the Kilby school teachers are of central causal significance to the - problems that the boys encounter. Furthermore, it is in response to these racist ideologies and practices that both West Indian and Asian pupils develop specific forms of collective resistance, which are seen to be linked to the wider black community, as legitimate strategies of survival.

Directly unproductive schooling:how country characteristics affect the impact of schooling on growth

The rapid rise in schooling in developing countries in recent decades has been dramatic. However, many cross-country regression analyses of the impact of schooling on economic growth find low and insignificant coefficients. This empirical 'puzzle' contrasts with theoretical arguments that schooling, through raising human capital, should raise income levels. This paper argues that poor results are to be expected when regression samples include countries that vary greatly in their ability to use schooling productively. Data on corruption, the black market premium on foreign exchange and the extent of the brain drain for developing countries are used as indicators of an economy's productive use of schooling. Regression analysis shows that the impact of secondary schooling on economic growth is substantially higher in countries that are adjudged to use schooling productivity.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

LIPPRED:a web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.

BPROMPT:a consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.