Heroes on the home front : heroism and virtue in post-9/11 American cinema /

This thesis is intended to contribute to critical discussion of the American male hero in mainstream American war and action films post September 11, 2001 . The thesis investigates how these heroes' behaviour echoes a patriotic, conservative construction of the modern American as created through speeches given by George W. Bush in the wake of the events of September 11, 2001 . The thesis examines the hero in six primary sources: the war films We Were Soldiers, Behind Enemy Lines and The Great Raid and the action films Collateral Damage, Man on Fire and The Punisher. By analyzing the ideological subtext, political content, visual strategies and generic implications of the films, as well as the binary constructions of a selection of Bush speeches, and by reviewing historical representations of American male heroes on film produced in the wake of political events, the thesis concludes that the six films mobilize the USA's conservative viewpoint towards war and military action, and in concert with the speeches, contribute to an ongoing militarization of visual culture. Both systems echo a dangerous ideological fantasy of American history, life and patriotism.

Test of Hockman and Oldham's job characteristics model in a post-secondary educational setting

One hundred and seventy-two subj ects participated in this quantitative, correlational survey which tested Hackman and Oldham's Job Characteristics Model in an educational setting. Subjects were Teaching Masters, Chairmen and Deans from an Ontario community college. The data were collected via mailed questionnaire, on all variables of the model. Several reliable, valid instruments were used to test the variables. Data analysis through Pearson correlation and stepwise multiple regression analyses revealed that core job characteristics predicted certain critical psychological states and that these critical psychological states, in turn were able to predict various personal and work outcomes but not absenteeism. The context variable, Satisfaction with Co-workers, was the only consistent moderating variable between core characteristics and critical psychological states; however, individual employee differences did moderate the relationship between critical psychological states and all of the personal and work outcomes except Internal Work Motivation. Two other moderator variables, Satisfaction with Context and Growth Need Strength, demonstrated an ability to predict the outcome General Job Satisfaction. The research suggests that this model may be used for job design and redesign purposes within the community college setting.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.