122221 SE Linguistics Seminar / BA Paper (2022S)
Identifying Gender Bias in Diachronic Corpora
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Tu 15.02.2022 00:00 to Th 24.02.2022 11:59
- Deregistration possible until Th 31.03.2022 23:59
Details
max. 18 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 07.03. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 14.03. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 21.03. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 28.03. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 04.04. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 25.04. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 02.05. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 09.05. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 16.05. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 23.05. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 30.05. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 13.06. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 20.06. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
- Monday 27.06. 14:15 - 15:45 Raum 2 Anglistik UniCampus Hof 8 3E-EG-09
Information
Aims, contents and method of the course
In contrast to biological sex, gender is a social construct, and it is constructed verbally. Language use, as reflected in corpora, reveals significant asymmetries in the ways in which genders are constructed. For example, the verb _to kill_ is much more likely to take _he_ as a subject than _she_, while _she_ is much more likely to be the subject of the verb _to dance_. Likewise, the possessive pronoun _her_ is much more likely than _he_ to be followed by the noun _children_, although every child has both a mother and a father. On the other hand, the noun _followers_ is much more likely to be preceded by _his_ than by _her_. In fact, there seem to be very few words use usage does not reveal biased conceptualisations of gender roles.At the same time, corpus evidence also reveals that usage has become significantly less gender-biased during the last 200 years. For example, the nouns _colleague_ and _job_ were hardly ever preceded by _her_ in the nineteenth century. Since the 1960ies, however, _her job_, and _her colleagues_ are about as common as _his job_, and _his colleagues_.In this course, we investigate the development of gender bias in large diachronic corpora.Our primary goal is to quantify changes in gender bias during the last two hundred years. Therefore, the first part of the course will offer an introduction to the use of diachronic corpora, and to the basics of statistical analysis in Spreadsheets like Excel.At the same time, however, we shall also look at our data also qualitatively, in order to avoid being misled by the evidence of mere numbers. For example, the mere frequencies of the phrases _he manages_ and _she manages_ may tell us very little, unless we know what it is that is managed by him or by her.Also, we shall have to discuss what asymmetric frequency distributions mean in each particular case. For example, the fact that the phrase _working mom_ is more frequent than the phrase _working dad_ does certainly not imply that it is considered more normal for mothers than for fathers to be working. Rather, the opposite is more likely.Thus, the goal of this course is twofold: on the one hand, we try to find out if language use has become more or less gender-biased over time, and on the other hand, we try to find out if corpus evidence is useful for addressing this question.
Assessment and permitted materials
Students are assessed on the basis of participation, assignments, project description, presentation and written seminar paper. Project description, presentation and seminar paper are based on the small-scale research project each student will select and work on during the semester.
Minimum requirements and assessment criteria
a) regular class attendance (max. 2 absences)
b) giving the oral presentation (on set date)
c) handing in project description & seminar paper (on time)
d) attaining 60 of the maximum 100 points.Course evaluation will be based on:
* class participation and assignments (max. 25 points)
* oral presentation (max. 15 points)
* seminar paper (max. 60 points)Final grades & points achieved: ‘1’: 90-100; ‘2’: 80-89; ‘3’: 70-79; ‘4’: 60-69; ‘5’: 0-59
b) giving the oral presentation (on set date)
c) handing in project description & seminar paper (on time)
d) attaining 60 of the maximum 100 points.Course evaluation will be based on:
* class participation and assignments (max. 25 points)
* oral presentation (max. 15 points)
* seminar paper (max. 60 points)Final grades & points achieved: ‘1’: 90-100; ‘2’: 80-89; ‘3’: 70-79; ‘4’: 60-69; ‘5’: 0-59
Examination topics
see 'minimum requirements'
Reading list
Baker, Paul. 2014. _Using Corpora to Analyze Gender_. London: Bloomsbury Academic.
Bolukbasi, Tolga, Kai-Wei Chang, James Zou, Venkatesh Saligrama & Adam Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. (8 May, 2021.)
Curzan, Anne. 2003. _Gender Shifts in the History of English_. Cambridge University Press.
Norberg, Cathrine. 2016. Naughty Boys and Sexy Girls. _Journal of English Linguistics_ 44, http://dx.doi.org/10.1177/0075424216665672.
Zhao, Jieyu; Wang, Tianlu; Yatskar, Mark; Ordonez, Vicente; Chang, Kai-Wei (2017): Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In: Martha Palmer, Rebecca Hwa und Sebastian Riedel (eds.): _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark. Stroudsburg, PA, USA: Association for Computational Linguistics, S. 2979–2989
Bolukbasi, Tolga, Kai-Wei Chang, James Zou, Venkatesh Saligrama & Adam Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. (8 May, 2021.)
Curzan, Anne. 2003. _Gender Shifts in the History of English_. Cambridge University Press.
Norberg, Cathrine. 2016. Naughty Boys and Sexy Girls. _Journal of English Linguistics_ 44, http://dx.doi.org/10.1177/0075424216665672.
Zhao, Jieyu; Wang, Tianlu; Yatskar, Mark; Ordonez, Vicente; Chang, Kai-Wei (2017): Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In: Martha Palmer, Rebecca Hwa und Sebastian Riedel (eds.): _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark. Stroudsburg, PA, USA: Association for Computational Linguistics, S. 2979–2989
Association in the course directory
Studium: BA 612
Code/Modul: BA06.2
Lehrinhalt: 12-2222
Code/Modul: BA06.2
Lehrinhalt: 12-2222
Last modified: Mo 07.03.2022 09:49