Some problems in the computation of sociolinguistic data

PhD ThesisThe research described in this thesis is concerned with some of the problems encountered in the processing of sociolinguistic data. Different methodologies are seen as different sets of strategies for coping with the problems which arise from investigations of sociolinguistic variability within any speech community. One early approach to the analysis of sociolinguistic variation (that of Labov: 1963, 1966) is discussed, and some of the difficulties raised by this approach are indicated. One investigation of sociolinguistic variability in a British urban setting (Trudgill: 1974) is also described (Trudgill' s study is based on Labov's (J 966) general methodology). FN The Tyneside Linguistic Survey (T.L.S.) is offered as an alternative approach, which overcomes some of the problems inherent in Labov's methodsThe Department of Education and Science, Newcastle University

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

The Silent Side of the Spectrum: Schizotypy and the Schizotaxic Self

The identification of individuals carrying unexpressed genetic liability to schizophrenia is crucial for both etiological research and clinical risk stratification. Subclinical psychopathological features detectable in the nonpsychotic part of the schizophrenia spectrum could improve the delineation of informative vulnerability phenotypes. Inspired by Meehl's schizotaxia-schizotypy heuristic model, we tested anomalous subjective experiences (self-disorders, SDs) as a candidate vulnerability phenotype in a sample of nonpsychotic, genetically high-risk subjects. A total of 218 unaffected members of 6 extended multiplex families (assessed between 1989 and 1999 during the Copenhagen Schizophrenia Linkage Study) were stratified into 4 groups of increasing psychopathological expressivity: no mental illness (NMI), no mental illness with schizotypal traits (NMI-ST), personality disorders not fulfilling other personality disorders (OPDs), and schizotypal personality disorder (SPD). We tested the distribution of SDs among the subgroups, the effect of SDs on the risk of belonging to the different subgroups, and the effect of experimental grouping and concomitant psychopathology (ie, negative symptoms (NSs) and subpsychotic formal thought disorder [FTD]) on the chances of experiencing SDs. SDs distribution followed an incremental pattern from NMI to SPD. SDs were associated with a markedly increased risk of NMI-ST, OPDs, or SPD. The odds of SDs increased as a function of the diagnostic category assignment, independently of sociodemographics and concomitant subclinical psychopathology (NSs and FTD). The results support SDs as an expression of schizotaxic vulnerability and indicate a multidimensional model of schizotypy—characterized by SDs, NSs, FTD—as a promising heuristic construct to address liability phenotypes in genetically high-risk studies

The heterogeneity of spouse abuse: A review

Recent research suggests that spouse abusers are not a homogenous group. Holtzworth-Munroe and Stuart (1994) propose three types of domestic violent men; Family Only, Generally Violent/Antisocial and Dysphoric/ Borderline Personality. This theoretical classification is compared to nine empirical research studies and two hypothetical studies, which can be found in the literature dated from 1994 onwards. The review provides support for the three-fold typology. The total averages of offenders classified by Holtzworth-Munroe and Stuarts (1994) typology across the studies were 50%, 30% and 20% respectively. However, when sample type was considered a significant difference between court referrals and volunteers was found for the men’s distribution across the three types, with court referred men being less likely to be categorised into the Family Only group. All research to date is criticised for its narrow focus on the offender and its lack of a scientific profiling approach. Other factors such as the context, triggers for violence and the behavioural actions of the victim need to be considered. A more holistic family focused typology is suggested to be more appropriate for those victims who do not want to separate from their violent spous

Studies in numerical taxonomy of soils

A series of established numerical taxonomic strategies was applied to soil data from three sources: USDA (1975), De Alwis (1971) and the Soil Survey of England and Wales. The first two sources provided data for 41 soil profiles, which were classified without reference to their geographical location. The data obtained from the Soil Survey of England and Wales related to a particular geographical area (West Sussex Coastal Plain) and the geographical relationship between soil individuals was also examined. Two methods of soil characterization (soil profile models) were compared with respect to their effect on the results produced by two hierarchical agglomerative strategies based on two measures of inter-individual similarity. Comparison of results, obtained from the agglomerative strategies for the two soil profile models, was made. The nature of inter-attribute correlation for depth levels modelled as arrays of independent attributes was examined, and all attributes were classified on the basis of inter-attribute correlation. Seven hierarchical agglomerative strategies were examined with respect to their goodness-of-fit in the original space and also the relationship between goodness-of-fit and clarity of clusters was examined. From these comparisons, two agglomerative strategies were chosen to represent two classes of strategy: (a) strategies with minimum of distortion, (b) strategies with a greater distortion but clear clusters. The average linkage method from the first category and the Ward's error sum of squares (ESS) method from the second category were selected. These two strategies were applied to the data sets described above using two measures of similarity namely (a) squared Euclidean distance and (b) Mahalanobis D2, and a divisive strategy, REMUL, was also applied to classify the soil populations. The classifications obtained from these strategies were compared by Wilk's Criterion A and the classification which had the lowest A was treated as the best initial partition. The best two partitions of the two populations obtained from the agglomerative strategy, Ward's ESS method, were further analysed. The optimum number of groups (G) in each population was decided by the relationship between LambdaG2 and G. The soil profile groups produced by these methods were further examined and improved by a reallocation strategy based on the Mahalanobis distance between individuals and the group centroids. Reallocation was done using 30 attributes from the uppermost soil horizons. Canonical analysis was performed on the populations both before and after the classification. Canonical plots were produced and a comparison was made with the dendrograms obtained for the best partitions. The classifications obtained were examined in relation to parent material classes. The spatial relationship of the soil groups of the West Sussex Coastal Plain was also investigated. As shown by this study, it is possible to produce a better classification of soils by numerical taxonomic methods compared with traditional methods. For this end, it is not necessary to use all attributes of soils, but a sufficiently large number of properties, which can be empirically determined, is adequate for the purpose of producing a natural classification. The soil groups produced by numerical methods showed a closer association with parent materials.<p

The prediction of future behavior: The empty promises of expert clinical and actuarial testimony

Testimony about the future dangerousness of a person has become a central staple of many judicial processes. In settings such as bail, sentencing, and parole decisions, in rulings about the civil confinement of the mentally ill, and in custody decisions in a context of domestic violence, the assessment of a person’s propensity towards physical or sexual violence is regarded as a deciding factor. These assessments can be based on two forms of expert testimony: actuarial or clinical. The purpose of this paper is to examine the scientific and epistemological basis of both methods of prediction or risk assessment. My analysis will reveal that this kind of expert testimony is scientifically baseless. The problems I will discuss will generate a dilemma for factfinders: on the one hand, given the weak predictive abilities of the branches of science involved, they should not admit expert clinical or actuarial testimony as evidence; on the other hand, there is a very strong tradition and a vast jurisprudence that supports the continued use of this kind of expert testimony. It is a clear case of the not so uncommon conflict between science and legal tradition

Its Reevaluation and Improvement As Related To A Profile Analysis Classification System

Over the first half of the present study the Mini-Mult (MM1), a brief form of the Minnesota Multiphasic Personality Inventory (MMPI), was compared with the standard MMPI in relation to eleven criteria classifications. These criteria were composed of the profile analysis classifications of Marks and Seeman (1963). Pearson product-moment correlations between the MM1 and MMPI failed to reach statistical significance for any of the eleven criteria. As based on the information gained, a correction factor was devised and added to the MM1. Subsequently, the second half of the study involved a comparison between the HMPI and the revised MM. This revised test was termed the MM2. In only two of eleven criteria cases did the correlation between the MM2 and MMPI reach statistical significance. However, for nine of the eleven criteria the MM2 did obtain a higher positive correlation with the MMPI than did the MM1. Such results indicate that the proposed correction factor is a step in the right direction and deserving of continued investigation. Although the MM2 as it presently stands cannot be validy substituted for the MMPI, continued research in the area of modifying its correction factor can yield it a useful clinical tool