Media, Gender, and Sexuality COMS 4604 A Wednesdays, 2:35 am-5:25 pm Mackenzie Bldg. 3190 Counting Women

1. Week 12, April 3, 2019
Media, Gender, and Sexuality
COMS 4604 A
Wednesdays, 2:35 am-5:25 pm
Mackenzie Bldg. 3190
Counting Women
Dr. Tracey P. Lauriault
Assistant Professor, Critical Media and Big Data
Carleton University
Tracey.Lauriault@Carleton.ca
orcid.org/0000-0003-1847-2738
@TraceyLauriault

2. Serendipity

3. Readings

4. Why do we count
things?

5. 2006
Counting makes things visible
2011

6. 2006
Counting makes things visible
2011

7. Counting makes things visible

8. Counting makes things visible

9. How we see things matters

10. How we see things matters

11. Quantifying things provides information

12. Quantifying things provides information

13. Quantifying things provides information

14. http://euclid.psych.yorku.ca/SCS/Gallery
/images/dan/quetelet-binomial.jpg
Counting & quantifying reveal the norm

15. http://euclid.psych.yorku.ca/SCS/Gallery
/images/dan/quetelet-binomial.jpg
Counting & quantifying reveal the norm

16. http://euclid.psych.yorku.ca/SCS/Gallery
/images/dan/quetelet-binomial.jpg
Counting & quantifying reveal the norm

17. Correlating things shows relationships

18. Correlating things shows relationships

19. Correlating things shows relationships

20. Correlating things shows relationships

21. Correlating things shows relationships

22. Correlating things shows relationships

23. When things are known actions can be taken
▪Obesity was considered a moral defect, biology,
research/science and the political economy of demographics
and locales have been shown to be factors associated with
obesity, it is now a social issue
▪Homosexuals were deviants and Genetics/science demonstrated
a biological predisposition, changing the moral argument
▪Air pollution leads to poor health, smog control & catalyzers
▪Unfortunately junk science can also lead to action! Creative
class, gay facial recognition, biased data or corpus, etc.
▪METHODOLOGY & CRITICAL THINKING

24. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
Bureaucracy acts upon known things

25. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
Bureaucracy acts upon known things

26. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
Bureaucracy acts upon known things

27. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
Bureaucracy acts upon known things

28. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
Bureaucracy acts upon known things

29. Classification & counting is resisted by those
counted

30. Classification & counting is resisted by those
counted

31. Classification & counting is resisted by those
counted

32. Classification & counting is resisted by those
counted

33. Classification & counting is resisted by those
counted

34. Social-shaping qualities of data
Kitchin, 2012

35. Dynamic Nominalism
Modified from Ian Hacking’s Dynamic Nominalism
Tracey P. Lauriault, 2012, Data, Infrastructures and Geographical Imaginations

36. Material Platform
(infrastructure – hardware)
Code Platform
(operating system)
Code/algorithms
(software)
Data(base)
Interface
Reception/Operation
(user/usage)
Systems of thought
Forms of knowledge
Finance
Political economies
Governmentalities - legalities
Organisations and institutions
Subjectivities and communities
Marketplace
System/process
performs a task
Context
frames the system/task
Digital socio-technical assemblage
HCI, Remediation studies
Critical code studies
Software studies
New media studies
Game studies
Theoretical approaches
Platform studies
Places
Practices
Flowline/Lifecycle
Surveillance Studies
Critical data studies
Algorithm Studies
Socio-Technological Assemblage
Modified by Lauriault from Kitchin, 2014, The Data Revolution, Sage.

37. http://kareningalasmith.com/counting-dead-women/
Counting Dead Women

38. Femicide Definition
Definition
“the killing of females
by males because they
are females.”
PATH, InterCambios, MRC, WHO
(2009) Strengthening
Understandings of Femicide: Using
Research to Galvanize Action and
Accountability. Washington DC.

39. • The Home Office now records and publishes data on homicide victims and the
relationship of the victim to the principal suspect and sex the of the victim.
• But it does not have the sex of the killer or connect different forms of male violence
against women.
Official Statistics

40. http://www.womensaid.org.uk/page.asp?section=00010001001400130010&sectionTitle=Femicide+
Census#femcensus
Femicide Census

41. http://www.womensaid
.org.uk/domestic-
violence-
events.asp?itemid=335
1&itemTitle=Femicide
+Census%3A+Profile
s+of+Women+Killed
+by+Men&section=0
00100010017&sectio
nTitle=Events+calend
ar
Authority &
Legitimacy

42. Why the Femicide Census?
1. Provide a clearer picture of domestic homicides in the UK by age/ethnic
origin/ relationship/ profession/region/outcome;
2. Provide a clearer picture of men’s fatal violence against women that is not
committed by a partner or ex-partner;
3. Information to create advocacy tools to provide concrete data on
domestic violence homicides;
4. Provide data when NGOs working to end domestic violence against
women is providing expert evidence on domestic homicides in civil
cases or before the Coroners court;
5. Provide comparisons and parallels between cases to identify where there
is the potential for a systemic argument against the State for failing to
protect the Right to Life; and
6. Provide a resource for academics researching femicides

43. Shelving Justice
▪ Action and Participatory Research project
▪ Qualitative interviews
▪ Archival records
▪ Ethnographic observations
▪ 1. Context about police lethality, excessive
force, neglect and mistrust in Detroit
▪ 2. Detailed description of how data are
collected from victims and how these move
through the system
▪ 3. List of state where there is a backlog of
untested rapekits
▪ 4. Discussed the failure:
▪ Serial rapists
▪ Exonerate the falsely accused
▪ Breach of trust
▪ Justice Denied
▪ 5. Research Questions
▪ Did the police know there were a large
number of untested kits in storage?
▪ Where they aware they had a problem?
▪ If not, how did they not know?
▪ If so, why did they not see this as a
problem?
6. Data
▪ Criteria to establish trustworthiness
▪ Credibility
▪ Transferability
▪ Dependability
▪ Confirmability
7. Described the problem
▪ Using their data
▪ Quotes from interviews
▪ Documentary evidence
(Campbell - 2015)

44. Shelving Justice
▪ Indicator – Crime lab closed due
to high error rate
▪ Action – Needed to examine
police evidence storage
▪ Accidental discovery of the kits
▪ The record keeping system
▪ did not flag this issue
▪ Evidence is logged and tagged
▪ Distributed storage
▪ When issue was flagged no action
was taken – Letters from the
Prosecutor’s office to Chief of Police
▪ Semantic wars – discovery, Numbers
Debate
▪ IA investigation;
▪ ‘random’ test of 36 kits + police reports
▪ Justifiable reasons?
▪ Victim’s fault were the reasons
▪ Perception of credibility
▪ Assumption that prostitutes cannot be
raped
▪ Complainant refused to prosecute
▪ ‘got what they got’
▪ ‘the assaults were not really rape’
(Campbell - 2015)

45. Shelving Justice = Justice Denied
▪The failure to test meant that:
▪Serial rapists continue to rape
▪The falsely accused are not exonerated
▪There is a breach of trust
▪ It is Justice Denied

46. Rape Kits Campaigns
Action

47. Rape Kits Campaigns
Action

48. Rape Kits Campaigns
Action

49. Rape Kits Campaigns
Action

50. Rape Kits Campaigns
Action

51. What matters?
▪Counting
▪Qualitative and quantitative data
▪Making things visible
▪Who & what & where
▪The way to count (methodology)
▪Accuracy, reliability, bias, objectivity, quality, completeness
▪Representation
▪Science and critical thinking
▪The story
▪Making the data work

52. Who Counts?
▪What would you like to see made visible with data?