American society possesses strong, if not basic, stereotypes for each hair color: the "dumb" blonde, the "intelligent" or "serious" brunette, and the "spitfire" redhead. In contemporary entertainment culture, blonde women have achieved unique status beyond the stereotypes accorded to their brunette and redheaded counterparts. Revered and reviled simultaneously, these women cannot be ignored or dismissed. The convention of the "dumb blonde" is at the heart of this issue. When scrutinized, it is possible to discern at least four distinctions of this stereotype: the perceived as truly dumb, or innocent, blonde (Johanna in Sweeney Todd); the bombshell blonde (Lorelei Lee in Gentlemen Prefer Blondes, Mae West in Dumb Blonde); the dumb-but-actually-intelligent blonde (Elle Woods in Legally Blonde, Galinda in Wicked); and the comedic blonde (Adelaide in Guys and Dolls). These characters presumably share more than their hair color and sex. By researching these blonde stereotypes, commonalities will be discovered and assessed for their applicability in character research. As this thesis explores the creation of Audrey in Ken Ludwig's Leading Ladies, a methodology for creating this type of character will be created. Through research and analysis of the various blonde stereotypes, an in-depth character and script analysis, and a journal of the creation process, it is my intention to reveal how a non-superficial portrayal of this character is possible and can be duplicated. Audrey's "blonde" traits will also be explored as they relate to the character's function within the play, emphasizing the ways her specified blondeness serves the play's needs. Analysis of the blonde stereotypes, script and character analyses, and the rehearsal journal will not only create a system for creating this type of character, but also will illuminate why this character type is important to comedic theatrical literature.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5155 |
Date | 01 January 2009 |
Creators | Young, Christine |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations |
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