| dc.contributor |
Matthew T. Zingraff, Committee Chair |
|
| dc.contributor |
Donald Tomaskovic-Devey, Committee Member |
|
| dc.contributor |
Patricia L. McCall, Committee Member |
|
| dc.contributor |
William R. Smith, Committee Member |
|
| dc.creator |
Miller, James Kirk |
|
| dc.date |
2010-04-02T19:16:55Z |
|
| dc.date |
2010-04-02T19:16:55Z |
|
| dc.date |
2003-07-30 |
|
| dc.date.accessioned |
2023-02-28T17:07:10Z |
|
| dc.date.available |
2023-02-28T17:07:10Z |
|
| dc.identifier |
etd-04302003-175917 |
|
| dc.identifier |
http://www.lib.ncsu.edu/resolver/1840.16/5632 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/265504 |
|
| dc.description |
The importance of race in explaining criminal justice processes and outcomes has been a research focus of sociologists interested in social control. In spite of conventional wisdom to the contrary, the expected negative effects of racial minority status on social control outcomes have been somewhat elusive in empirical tests. The practice of racial profiling, defined as the use of race by police in decision-making and especially traffic stops, is at the forefront of contemporary public concern about policing, racial discrimination, and public safety.
The dissertation begins to address the open questions about racial profiling by developing and testing a multilevel conceptual model of police traffic stops. The conceptual model focuses on four distinct sources of police decision-making and behavior: suspect or driver characteristics, legal or driving behaviors, organizational characteristics of the police, and community contextual characteristics.
The research incorporates survey data collected in 200 and 2001 on 1445 licensed Black and 1475 licensed white drivers in North Carolina with 1990 and 2000 census data and criminal justice data spanning the 1997-2000 time period. The survey data contain measures of driver characteristics and driving practices along with geographic markers which allow the individual level data to be linked with community data sources. Hierarchical linear modeling (HLM) techniques are used to separately model driver reported traffic stops by local police agencies and by the North Carolina State Highway patrol (NCSHP).
Key aspects of the conceptual models are confirmed by HLM models of local police stops which suggest that driver characteristics which compose social threat are important to increases in the risk of experiencing a traffic stop. Driver race, gender, and age are important predictors of increases in traffic stop risk, while many driving factors do not appear to be related to the risk of a traffic encounter with local police. In contrast, models of NCSHP stops suggest that stop risk is increased for those who self-report higher level of illegal driving behaviors. Driver race and gender are not related to stop risk by the NCSHP. Evidence for contextual effects is mixed. Implications for current and future police research are discussed. |
|
| dc.rights |
I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
|
| dc.subject |
racial profiling |
|
| dc.subject |
police |
|
| dc.subject |
race |
|
| dc.subject |
bias |
|
| dc.subject |
social control |
|
| dc.title |
Race, Place, Cops and Stops: Local Context, Racial Profiling, and Social Control in North Carolina. |
|