Preventing Terrorism in the Long Term: The Disutility of Racial Profiling in Preventing Crime and the Counterproductive Nature of Ethnic and Religious Profiling in Counterterrorism Policing
Abstract : After the terrorist attacks on September 11, 2001, the desire of Americans to feel secure made ethnic and religious profiling a tempting security trade-off. Generalizations about Arabs and Muslims as terrorists seemed to lead to an increasing practice of singling out individuals who look Arab or appear to be Muslim in entry-exit systems and in counterterrorist investigations. Civil liberties and Muslim advocacy groups immediately cried foul as accusations of profiling began to surface in the media and various government reports. Today, the main emphasis of the debate continues to focus on civil liberties. The aim of this thesis is to take a fresh perspective on profiling in counterterrorist-operations and demonstrate that profiling is actually counterproductive to an effective long-term counterterrorism strategy. This thesis first highlights major findings on the usefulness of racial profiling in criminal policing. It then examines issues of ethnic identity and the grand strategy of Islamic terrorist organizations and illustrates the counterproductive nature of ethnic and religious profiling. It will also demonstrate that while complaints of ethnic profiling persist within the Muslim community, the technique does not appear to have played a role in the disruption of actual terrorist plots in the United States.
THE SMELL OF FEAR
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For many of us, when faced with a dangerous situation, our first reaction might be to yell and scream or in some way vocally communicate the danger to others. However, unbeknownst to us, we are also likely communicating the danger in a more subtle way. Plants and animals have
Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes
AbstractThe aim of this paper is to examine the performance of the geographically weighted regression (GWR) technique in terms of recognizing linear relationships at the local level. Using a Monte Carlo analysis with two independent spatially autocorrelated SAR(1) processes, this study finds that spurious behavior exists at the local level along with spatially autocorrelated errors. Therefore, GWR estimation results must be interpreted with caution.
