Musically Consonant, Socially Dissonant: Orange Walks and Catholic Interpretation in West-Central Scotland

This article examines the music used by the Orange Order, in its public parades, more commonly referred to as “Orange Walks.” The Orange Order is an exclusively Protestant fraternal organization, which traces its roots to 1690 and the victory of the Protestant Prince William of Orange over the Catholic King James. Yet, as in Northern Ireland, many consider the group to be sectarian and view its public celebrations as a display of ethno-religious triumphalism. This article explores the extra-musical factors associated with Orangeism’s most iconic song, “The Sash My Father Wore,” how other groups have misappropriated the song, and how this has distorted its meaning and subsequent interpretation.

Recent statistics have shown that Glasgow hosts more Orange parades each year than in Belfast and Derry/Londonderry combined, yet while there have been many anthropological and ethnomusicological studies of Northern Ireland’s Orange parades, very little research has focused on similar traditions in Scotland. This article seeks to address that gap in the literature and is intended as a preparatory study, laying the groundwork for further analysis.

Random Forest Based Approach for Concept-Drift Handling

Algorithms for concept drift handling are important for various applications including video analysis and smart grids. In this paper we present decision tree ensemble classication method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with îriginal random forest with incorporated replace-the-looser forgetting andother state-of-the-art concept-drift classiers like AWE2.