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This is the abstract for my dissertation, titled Gender/Genre: Gender differencein disciplinary communication, which I am scheduled to defend on April 24, 2015. Though the dissertation will be available online through the University of Minnesota University Digital Conservancy beginning in May 2017, until then, anyone wanting a copy should contact me.
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Abstract
Within the professions, writers are expected to express themselves in certain ways,
often within genres that are bound by conventions, including linguistic register. The
student entering a profession learns those genres as if they are mandatory and static,
and conforming or failing to conform to conventions is believed to have ties to career
consequences. However, new members of a profession come to it with other habitual
language practices aectedaccording to previous researchby the writers gender.
Rhetorical genre theory and disciplinary, professional, and technical communication
theory do not oer a full account for the ways in which these old habits and new
conventions must interact, and previous research in gender and language does not fully
account for how gendered persons write when confronted with high-stakes convention-
bound writing tasks. I used tools from statistics and natural language processing (NLP)
to assess stylistic features that previous research has associated with gender dierences
in written language: I applied those tools to texts created by law students near the end
of their first year of study in the genre of a court memorandum, and I found there was
no pattern of dierence between male and female writers in these texts.
I propose a cognitive pragmatic rhetorical (CPR) theory, grounded in work of
Straheim (2010), who attempted to bridge the relevance philosophy of Alfred Schutz
(Schutz, 1973, 1964, 1966) and the Relevance Theory of Sperber and Wilson (1995);
I have extended Straheims work with insights from rhetoric and cognitive science.
CPR theory explains that these apprentice members of a professional community will
expend great eort to conform to its conventions and genres because of the students
goals and the practical eects that depend on conformity. Consequently, we expect
them to abandon gendered linguistic habits, at least while they are engaged in early
training. This dissertation demonstrates a methodologically rigorous gender-dierence
study; oers evidence for an anti-essentialist view of gender dierences in communi-
cation; and gives insight into the process by which apprentice members of a profession
adjust their communicative processes in response to their training. It demonstrates the
utility of CPR theory and NLP tools in scholarly inquiries in rhetoric and disciplinary,
professional, and technical communication.
iv
AcknowledgementsDedicationAbstractList of TablesList of FiguresIntroductionOverviewPreview of following chapters
Cognitive pragmatic rhetorical theory: A frameworkIntroductionMetatheoretical concernsEpistemic commitmentsWhy ``rhetorical,'' ``pragmatic,'' and ``cognitive''?
``Classical'' pragmatics, rhetoric, and cognitionOverview of classical pragmaticsClassical pragmatics and rhetoricClassical pragmatics and cognitive science
Relevance-theoretic pragmaticsSWRT: The relevance theory of Sperber and WilsonSSRT: Straheim's extension of relevance theory
Cognitive pragmatic rhetorical theoryComponents of cognitive environmentsCPR-theoretic production and interpretation
Conclusion
Gender differences in writers' choicesIntroductionShould we do gender-difference studies?The Argamon/Koppel 02/03 studyThe Koppel et al. 2002 machine-learning studyThe Argamon et al. 2003 statistical studyLimitations of the Argamon/Koppel 02/03 study
Gender in studies of gender-differenceMaking gender operational in other studiesA framework for operationalizing gender
Genre in studies of gender-differenceDefining ``genre''Rationale for studying genreMethodological options for exploring genreQuestions and problemsMaking genre operational in other studiesA framework for operationalizing genre knowledge and genres
The single-author problemConclusion: Do men and women write differently?
Study design: Seeking gender differences in genred writingIntroductionLaw school contextTexts in a professional genreTexts by single authorsAuthors who identify their own genders
Data collection and preparationData CollectionData preparation: Annotating, splitting, tokenizing, tagging, and counting
Data analysisStatisticsMachine learning
Ethical considerationsConclusion
Findings: Gender similarity in genred writingIntroductionFindings from statistical analysesComparing and contrasting the Argamon et al. 2003 findingsSignifcant differences in this studyFindings regarding research questions 1 and 2
Findings from machine-learning analysesTrials with full 986-feature data setsTrials with reduced feature setsThe search for patterns in reduced feature setsFindings regarding research questions 3 and 4
Conclusion
Discussion: CPR theory and gender/genreIntroductionReprise of CPR theoryCPR theory in context in this studyCPR theory accounts for gendered languageCPR theory accounts for genre knowledgeCPR theory explains findings in the present studyConclusion
ConclusionIntroductionLimitations of this studyImplications, applications, and potential criticismsImplications of this studyImplications and applications of CPR theoryPotential shortcomings of CPR theory
Questions for future researchConclusion
References Appendix A. Project materials deposited in the University of Minnesota Digital Conservancy Appendix B. Part-of-speech tags Appendix C. Function words used in the Argamon/Koppel 02/03 studyand in the present study Appendix D. Research information form for study participants Appendix E. Student survey instrument Appendix F. Demographics of student participantsGender self-identificationOther demographics
Appendix G. Data preparationManual annotation of textsProcessing in Python and NLTKExport of data to ARFF files for WEKACoding guides for manual annotation
Appendix H. Examples of bigram and trigram features in context Appendix I. Frequency values for all features in the present studyOverview of table contents
Appendix J. Findings from machine learning trialsTrials with the Winnow algorithmTrials with other linear approachesTrials with instance-based classifiersTrials with support vector machinesTrials with Naive Bayes modelsSummary of machine learning trial results