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Counting Women

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

Publié dans : Technologie
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Counting Women

  1. 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. 2. Serendipity
  3. 3. Readings
  4. 4. Why do we count things?
  5. 5. 2006 Counting makes things visible 2011
  6. 6. 2006 Counting makes things visible 2011
  7. 7. Counting makes things visible
  8. 8. Counting makes things visible
  9. 9. How we see things matters
  10. 10. How we see things matters
  11. 11. Quantifying things provides information
  12. 12. Quantifying things provides information
  13. 13. Quantifying things provides information
  14. 14. http://euclid.psych.yorku.ca/SCS/Gallery /images/dan/quetelet-binomial.jpg Counting & quantifying reveal the norm
  15. 15. http://euclid.psych.yorku.ca/SCS/Gallery /images/dan/quetelet-binomial.jpg Counting & quantifying reveal the norm
  16. 16. http://euclid.psych.yorku.ca/SCS/Gallery /images/dan/quetelet-binomial.jpg Counting & quantifying reveal the norm
  17. 17. Correlating things shows relationships
  18. 18. Correlating things shows relationships
  19. 19. Correlating things shows relationships
  20. 20. Correlating things shows relationships
  21. 21. Correlating things shows relationships
  22. 22. Correlating things shows relationships
  23. 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. 24. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf Bureaucracy acts upon known things
  25. 25. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf Bureaucracy acts upon known things
  26. 26. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf Bureaucracy acts upon known things
  27. 27. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf Bureaucracy acts upon known things
  28. 28. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf Bureaucracy acts upon known things
  29. 29. Classification & counting is resisted by those counted
  30. 30. Classification & counting is resisted by those counted
  31. 31. Classification & counting is resisted by those counted
  32. 32. Classification & counting is resisted by those counted
  33. 33. Classification & counting is resisted by those counted
  34. 34. Social-shaping qualities of data Kitchin, 2012
  35. 35. Dynamic Nominalism Modified from Ian Hacking’s Dynamic Nominalism Tracey P. Lauriault, 2012, Data, Infrastructures and Geographical Imaginations
  36. 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. 37. http://kareningalasmith.com/counting-dead-women/ Counting Dead Women
  38. 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. 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. 40. http://www.womensaid.org.uk/page.asp?section=00010001001400130010&sectionTitle=Femicide+ Census#femcensus Femicide Census
  41. 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. 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. 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. 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. 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. 46. Rape Kits Campaigns Action
  47. 47. Rape Kits Campaigns Action
  48. 48. Rape Kits Campaigns Action
  49. 49. Rape Kits Campaigns Action
  50. 50. Rape Kits Campaigns Action
  51. 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. 52. Who Counts? ▪What would you like to see made visible with data?

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