Learning to see hate crimes : a framework for understanding and clarifying ambiguities in bias crime classification

Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12389/20097

Title:	Learning to see hate crimes : a framework for understanding and clarifying ambiguities in bias crime classification
Authors:	Nolan, James J., III McDevitt, Jack Cronin, Shea Farrell, Amy
Subject Keywords:	hate crime ; data collection ; law enforcement
Issue Date:	Mar-2004

File	Description	Size	Format
03367.pdf		137.44 kB	Adobe PDF	See online View/Open

Key Issues:	Hate crime
Publisher:	Routledge
Publication Country:	United States
Publication Place :	New York, NY
Material Type:	tools and guides
Language:	English
Host item:	Criminal Justice Studies
Host item vol.no:	vol. 17, no. 1, p. 91-105
Country:	United States
Country Coverage:	United States
URL more information:	http://www.informaworld.com/smpp/content~db=all~content=a713643792~tab=content
Abstract:	Recent empirical research has identified ambiguity in bias crime reporting as a source of confusion and frustration in law enforcement agencies and as a source of error in the national hate crime statistics. The authors develop a framework for understanding and clarifying these ambiguities based on John Dewey's conception of intension and extension and their own application of mathematical set theory to the issue. The authors discuss the implications of their model for helping law enforcement officials see bias crimes for varied purposes, including prevention, statistical reporting, and criminal prosecution.
Physical Description:	15 p.
URI:	http://hdl.handle.net/20.500.12389/20097
ISSN:	1478-601X
Appears in Collections:	Resources on hate crime Documents