"Brethren upon the same level" : membership and class in Calumet's Masonic Lodge

The Keweenaw Peninsula of Upper Michigan was a ethnic conglomerate of cultures and ideas, with people attracted to the area by the mineral wealth found along the Copper Range. The center of copper mining from the mid 1860s to 1968 was in the vicinity of Calumet Township, home to the world-famous Calumet and Hecla Mining Company. The township depended on the mines and the company’s president Agassiz’s strove to make the area a “model community,” that included groups such as the Free and Accepted Masons. Men from myriad backgrounds arrived in Calumet from the British Isles, Germany, Finland, Eastern and Southern Europe and the Eastern United States. As in other communities from the time period these men formed common interest groups like Masonic Lodge 271, which received its charter in 1870. Gentlemen joined with merchants and craftsmen. They became “brethren upon the same level,” and were elevated to the status of Master Mason. This symbolic transformation within the Lodge removed the men from the “profane world” outside the sanctity of Masonry, and in the ritualistic transformation of the meeting they were reborn into Masonry’s sacred mysteries. Masonry acted as a means of moral guidance to men and gave them access to a larger social and economic community through a common connection of brotherhood. As the candidates moved through the three Blue Lodge degrees of Entered Apprentice, Fellowcraft, and Master Mason they saw each other as “brethren upon the same level” – all economic classes equal within the Masonic Lodge. To examine equality within Lodge 271, this study sorted workers into classes to allow a comparison of Lodge 271’s membership. Possibly a comparison between other lodges can be drawn from the membership. The Union Building in Calumet, MI will be examined for its role in the ritualistic transformation of Masonry as it housed Masonic activities and transformations. This transformation brought men into the lodge of brothers. While Masonry professed equality between members however, to what extent did the membership of the lodge reflect this between the brethren? To what extent did economic class determine who was made “brethren upon the same level? 1 Arthur Thurner, Calumet Copper and People: History of a Michigan Mining Community, 1864-1970 (Hancock, MI: Book Concern, 1974), 122.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

AUTOMATIC LABELING OF RSS ARTICLES USING ONLINE LATENT DIRICHLET ALLOCATION

The amount of information contained within the Internet has exploded in recent decades. As more and more news, blogs, and many other kinds of articles that are published on the Internet, categorization of articles and documents are increasingly desired. Among the approaches to categorize articles, labeling is one of the most common method; it provides a relatively intuitive and effective way to separate articles into different categories. However, manual labeling is limited by its efficiency, even thought the labels selected manually have relatively high quality. This report explores the topic modeling approach of Online Latent Dirichlet Allocation (Online-LDA). Additionally, a method to automatically label articles with their latent topics by combining the Online-LDA posterior with a probabilistic automatic labeling algorithm is implemented. The goal of this report is to examine the accuracy of the labels generated automatically by a topic model and probabilistic relevance algorithm for a set of real-world, dynamically updated articles from an online Rich Site Summary (RSS) service.