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COMS5225 Critical Data Studies

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Week 13 (Apr. 8) – Assemblages, Genealogies and Dynamic Nominalism

Course description:

The emphasis is to learn to envision data genealogically, as a social and technical assemblages, as infrastructure and reframe them beyond technological conceptions. During the term we will explore data, facts and truth; the power of data both big and small; governmentality and biopolitics; risk, probability and the taming of chance; algorithmic culture, dynamic nominalism, categorization and ontologies; the translation of people, space and social phenomena into and by data and software and the role of data in the production of knowledge.
This class format is a graduate MA seminar and a collaborative workshop. We will work with Ottawa Police Services and critically examine the socio-technological data assemblage of that institution. This includes a fieldtrip to the Elgin street station; a tour of the 911 Communication Centre and we will meet with data experts.

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COMS5225 Critical Data Studies

  1. 1. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 COMS5225 Assemblages, Genealogies and Dynamic Nominalism Week 13: Critical Data Studies 08, April 2019 Class Schedule: Mondays, 11:30 - 14:30 Location: HZB5437 Instructor: Dr. Tracey P. Lauriault E-mail: Tracey.Lauriault@Carleton.ca Office: 4110b River Building Office Hours: Tuesdays 13:00-16:00 ORCID:0000-0003-1847-2738 CU IR: https://ir.library.carleton.ca/ppl/8
  2. 2. 13 Weeks – 36 Hours Weeks Date Assignments Week 1 – What are data? Jan. 7 Week 2 – Assemblages, Indicators & Performance Measures Jan. 14 Assignment 1: Description Week 3 – Facts Jan. 21 Week 4 – Field Trip Ottawa Police Services Jan. 28 3.1 Field Trip Week 5 – Categories and Social Sorting Feb. 4 3.2 Data Assemblage Brainstorm Week 6 – Administrative and Survey Data Feb. 11 3.3 Paper & Poster Quad Chard Study Break Feb. 18-22 Week 7 – Standards Feb. 25 3.4 DRAFT Paper Outline + Poster Abstract Mar. 1 3.5 Submit Poster Abstract to CUIDS Week 8 – Mapping & Indigenous Knowledge Mar. 4 Assignment 4: In Library Assignment Week 9 – Big Data Mar. 11 3.6 DRAFT Poster for Peer Review Week 10 – Probability & Big Data, Mapping, Poster Mar. 18 3.7 Print Poster & Submit to CULearn (Mar. 22 or even Sunday Mar. 24 night!) Week 11 – Data Infrastructure, Probability & Risk Mar. 25 3.8 DRAFT Research Paper for Peer Review Data Day 6.0 Mar. 26 3.9 Data Day 6.0 Week 12 – From Critical Theory to Action Apr. 1 3 Films Week 13 – Assemblage, Genealogies & Dynamic Nominalism Apr.15 3.10 Submit Final Research Paper
  3. 3. Week 1 – What are data? http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 1 Conceptualizing Data Ch. 7 Data Ch. 1 Numbers, Power & Organization Ch. 21 Ch. 1 Statistics as Social Science
  4. 4. Week 1 – Thematic Readings http://doi.org/10.22215/tplauriault.courses.2019.coms5225 ▪ Gartner, Rosemary (2015) Crime: Knowledge about and Prevalence. In International Encyclopaedia of the Social & Behavioral Sciences, 164–69. Elsevier, 2015. https://doi.org/10.1016/B978-0-08-097086-8.45004-X ▪ Hughes, Lorine A., and James F. Short. (2015) Crime, Sociology Of.” In International Encyclopaedia of the Social & Behavioral Sciences, 189–93. Elsevier, https://doi.org/10.1016/B978-0-08-097086-8.45016-6.
  5. 5. 1. Data Description and Conceptualization- Due Week 2, Jan. 14, 10:00AM (10%): Select a dataset in the wild related this year’s theme. In a total of 3 pages describe these data technically and in such a way that 10 years from now you will be able to decipher the nature of these data. ▪ Technical descriptions of data generally include the following, but do not be limited to this: consider format, sample size, headings, metadata, licences and terms of use, how are they disseminated, who is the publisher, the producing institution, data authors if there are any, methodology, dates, geography, classifications, models, methods, etc. ▪ Be sure to cite the dataset & provide the URL, be sure to cite any related documentation, you can use footnotes, images and tables if useful, but do use full citation, captions and document styles. Get to know these data. ▪ You will also conceptually frame these data according to Kitchin's 4 remaining conceptualizations and identify any elements of the socio-technological assemblage. This can be done in a table. ▪ State why you are interested in this dataset, what you might use the data for, how the data are conventionally used and explain what led you to trust them. ▪ NOTE: Images, tables and references will not go against your page count. Think of this as a critically informed lab report. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  6. 6. Assignment 1 - Data Description 1. Police Reported hate crime, be census metropolitan area, Canada https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3510019101 2. Ottawa Police Service (OPS) Incident http://moto.data.socrata.com/dataset/Ottawa-Police/uacw-px76 3. Community Well-Being Index (for 1991) http://www.aadnc- aandc.gc.ca/DAM/DAM-INTER-HQ-AI/STAGING/texte- text/cwb_2006_1452011133706_eng.csv 4. Reveal’s RS3-SX Body Camera https://www.revealmedia.com/police-body-worn- cameras 5. Uniform Crime Reporting Incident-Based Survey (UCR): Violations and Relation to Victim, Grouped by Age and Sex https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=hdl:10864/11603 6. Police officers by rank and gender, municipal police services https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3510019101 http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  7. 7. Week 1 – Reading Reflection Questions ▪How are data usually explained in popular discourses? ▪How does this week 1 exploration and description of data vary from the ones you may have heard before? ▪Why do you think that data are often misunderstood when explored in popular discourses? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  8. 8. Week 2 - Assemblages, Indicators & Performance Measures http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Hammer, Ch. 4 Immervol, Ch. 9
  9. 9. Week 2 - Thematic Readings http://doi.org/10.22215/tplauriault.courses.2019.coms5225 ▪ Gartner, Rosemary (2015) Crime: Knowledge about and Prevalence. In International Encyclopaedia of the Social & Behavioral Sciences, 164–69. Elsevier, 2015. https://doi.org/10.1016/B978-0-08-097086-8.45004-X ▪ Hughes, Lorine A., and James F. Short. (2015) Crime, Sociology Of.” In International Encyclopaedia of the Social & Behavioral Sciences, 189–93. Elsevier, https://doi.org/10.1016/B978-0-08- 097086-8.45016-6. ▪ Deflem, Mathieu, and Samantha Hauptman. (2015) Policing. In International Encyclopaedia of the Social & Behavioral Sciences, 260–65. Elsevier, https://doi.org/10.1016/B978-0-08-097086- 8.45007-5. ▪ McCall, Patricia L., and Joshua A. Hendrix. (2015) Crime Trends and Debates. In International Encyclopaedia of the Social & Behavioral Sciences, 194–202. Elsevier, https://doi.org/10.1016/B978-0-08-097086-8.45050-6.
  10. 10. Week 2 - Thematic Materials ▪ Ottawa Police Service (2017) Annual Report, https://www.ottawapolice.ca/en/annual-report-2017/resources/2017/OPS-2017- Annual-Report-Online.pdf ▪ City of Ottawa (2017) Budget, https://ottawa.ca/en/news/budget-2017#adopted- budget-2017-alternative-accessible-format ▪ Canadian Association of Chiefs of Police, Police Information and Statistics (POLIS) Committee, https://www.cacp.ca/police-information-and-statistics-polis- committee.html ▪ Public Safety of Canada (2015) Measuring the Performance of the Police: The Perspective of the Public, Research Report: 2015-R034 by Anton Maslov https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/2015-r034/2015-r034-en.pdf ▪ Public Safety Canada, Research Summary: Police Performance and Surveys, https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/2015-s034/index-en.aspx ▪ Statistics Canada, (2018) Police Resources in Canada, https://www150.statcan.gc.ca/n1/pub/85-002-x/2018001/article/54912-eng.htm http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  11. 11. Week 2 - Indicator References http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 3 & 4 Introduction
  12. 12. Week 2 – Reading Reflection Questions ▪What would be the best way to track citizen satisfaction with the police? ▪Is there a particular form of data collection which would be more accurate to represent citizen sentiment? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  13. 13. Week 3 - Facts http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 2 pp. 15-41 & 89-103 Ch. 6 Ch. 3 Executive Summary
  14. 14. Week 3 - Thematic Readings http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  15. 15. Week 3 - Thematic Materials ▪Ottawa Police Service (2017) Annual Report, https://www.ottawapolice.ca/en/annual-report-2017/resources/2017/OPS- 2017-Annual-Report-Online.pdf ▪Public Safety and Emergency Preparedness Canada (2004) Public Confidence in the Criminal Justice System, Research Summary, https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/pblc-cnfdnc/pblc-cnfdnc- eng.pdf ▪Statistics Canada (2014) General Social Survey of Victimization, https://www150.statcan.gc.ca/n1/pub/85-002-x/2015001/article/14241- eng.htm ▪Toronto Police Service Public Safety Data Portal http://data.torontopolice.on.ca/pages/major-crime-indicators http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  16. 16. Week 3 - Critical Thinking Material http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  17. 17. CRAP Test Currency ▪ Site or page date ▪ Is the date of publication or last revision published (often at the bottom of the page)? ▪ When was the site or page last updated? Reliability ▪ Evidence of the peer review process (e.g., in an "About us" or editorial statement) ▪ A bibliography or reference list Authority ▪ Author's credentials ▪ Look for information about the author of the site or page. ▪ Is the author qualified to publish on this topic? ▪ E.g, Can you identify the author's education and relevant professional experience? ▪ Look up the author's name in the Carleton University Library catalogue or Wikipedia. ▪ URL ▪ Read the uniform resource locator (URL) carefully to determine if you are reading someone's personal page. ▪ You need to investigate the author carefully because personal pages have no publisher or domain owner to vouch for the information. ▪ Domain ▪ Is the domain extension appropriate for the content? ▪ Government sites: .gov ▪ Educational sites: edu ▪ Nonprofit organizations: .org Publisher ▪ Identify the publisher (individual or organization) of the site or page. ▪ The publisher operates the server computer from which the site or page is issued. Do you know anything about the publisher? "About us" links ▪ Read the information on the site or page about the author and/or publisher. ▪ This could be under "about us," "philosophy," "background," or "bibliography" tabs. Page design or structure ▪ Page design is not always an indicator of credibility but if a site or page is easy to navigate, you'll be able to assess the information more easily. Purpose/Point of view ▪ "About us" links ▪ Read the information on the site or page about the author and/or publisher. ▪ This could be under "about us" or "philosophy", "background" or "bibliography" tabs. ▪ Is there advertising? Cross reference information ▪ Try to verify the information by cross referencing the material. ▪ Look up some of the references in Google Scholar (through the Carleton University Library). http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  18. 18. We’re hardwired to believe…. ▪Psychologists, criminologists, cognitive scientists suggest that we are hardwired to think in certain ways ▪And Lawton, hints that to be critical thinkers we need to work around this wiring 1. Zero sum – win-win 2. Folk knowledge 3. Stereotyping 4. Sycophancy 5. Conservatisms 6. Tribalism 7. Religion 8. Revenge 9. Fonfabulations http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  19. 19. Week 3 – Reading Reflection Questions ▪To what degree should police research/findings be reproducible due to their hyper specific context? Can a social phenomena be replicated? ▪How are ‘facts’ or definitions conceptualized at a police department, and how are they (if they are) standardized? ▪Would it be beneficial for police forces to have a standardized research methods and definitions, and to what scale (provincial, country, globally, etc.)? ▪How can we change “who counts” in police force data, where marginalized people seem over-represented? ▪Who gets to count the police? ▪Legitimacy? ▪If the way in which we interact with the world is based on our personal perceptions, is there such a thing as objectivity? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  20. 20. Week 4 - OPS Field Trip http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  21. 21. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Post OPS Visit Brainstorm
  22. 22. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Colour coding legend: - Darker blue – main topic - Lighter blue – main components of assemblage - Purple – bigger ideas or concepts to unpack - Pink – interesting things to note/particularly stood out to me - Black – smaller aspects/questions to ask Post OPS Visit Brainstorm
  23. 23. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Post OPS Visit Brainstorm
  24. 24. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Post OPS Visit Brainstorm
  25. 25. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  26. 26. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Post OPS Visit Brainstorm
  27. 27. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Post OPS Visit Brainstorm
  28. 28. Week 5 - Categories and Social Sorting http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 9 Making Up People Ch. 1 & 7
  29. 29. Week 5 - Thematic Readings http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 4 Ch. 8
  30. 30. Week 5 - Thematic Materials http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  31. 31. Week 5 – Reading Reflection Questions ▪From our trip to OPS, did we see any instances of boundary work across OPS departments (e.g. police & coms center) or between the OPS and other services (e.g. Stats Can)? How did this affect their operations/data collection/database reliability? ▪How would you define racial categories? ▪Is there a difference between self-categorization and categorization imposed by a legal governing body? ▪And who gets to decide what categories are legitimate? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  32. 32. Week 6 - Administrative and Survey Data http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 1 & 2 Ch. 2, Desrosiere pp.201-222 Ch. 4 Ch. 14
  33. 33. Week 6 – Thematic Readings http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  34. 34. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  35. 35. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  36. 36. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  37. 37. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  38. 38. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  39. 39. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  40. 40. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Proposal
  41. 41. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  42. 42. Week 6 – Reading Reflection Questions ▪ Considering Curtis’ (2002) analysis of making census population and critically thinking about the ethics behind Microdata linkages and the issue surrounding privacy, do you think that public good outweighs privacy intrusion? ▪ What might differ between government and policing institutions in the way they think about and use data, security concepts, economic knowledge? And is this the same everywhere? ▪ Who are and what can we say the government (and policing) is, in a time when it encompasses so much, across so many institutions, geographies, platforms, and actors? ▪ Further, how is accountability to be handled when acts of government (and policing) are so far removed from actors? ▪ How do police conceptualize themselves according to Foucault’s models (justice, administrative, or government), if at all? How might these be reflected in their performance measurements? The “balanced model” for example, “properly consider[s] cost versus results and quality trade-offs” (Kiedrowski et. al, 2013, p. 16). Is this a turn away from singular conceptualizations of responsibility? Towards a “whole complex of knowledges” (Foucault, 1994, p. 220)? ▪ Would an uniformed set of standards and guidelines help governments to use data better? Or should different sets of data each be contextualized in their own space for their specific purpose? What questions need to be asked about data before we can use them in a manner that takes into account the fact that they are not neutral? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  43. 43. Week 7 - Standards http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Ch. 3 Ch. 2 pp. 3-35 & 149-177 Ch. 12
  44. 44. Week 7 - Thematic Materials ▪ Canadian Centre for Crime Statistics, Revising the classification of founded and unfounded criminal incidents in the Uniform Crime Reporting Survey https://www150.statcan.gc.ca/n1/pub/85-002- x/2018001/article/54973-eng.htm ▪ Ottawa Police Service (2016) Regulated Interactions, https://www.ottawapolice.ca/en/news-and- community/RegulatedInteractions.aspx ▪ Ottawa Police Service (2017) Annual Report: COLLECTION OF IDENTIFYING INFORMATION – DUTIES & PROHIBITIONS POLICY: ANNUAL REPORT https://www.ottawapolice.ca/en/about- us/resources/Regulated_Interactions_2017Annual_Rep ort_Final.pdf ▪ United Nations Office on Drugs and Crime, Standards and Manuals, https://www.unodc.org/unodc/en/data- and-analysis/standards-and-manuals.html ▪ Open Corporates https://opencorporates.com/info/about ▪ IATI http://www.aidtransparency.net/ http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  45. 45. Week 7 - What is a standard? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  46. 46. Draft Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  47. 47. Draft Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  48. 48. Draft Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  49. 49. DRAFT Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  50. 50. Draft Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  51. 51. Draft Abstract http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  52. 52. Week 7 – Reading Reflection Questions ▪ Who gets to decide what standards should exist? How might standards actually work against their goal of objectivity depending on who decides what they are going to be and how are they operationalized? ▪ Creation of an increasingly “known” citizen and subsequent shared (standard) identity. ▪ IDs as forms of knowledge in itself (generating new categories of data, becoming a uniform way to “know” people). ▪ The way ID mechanisms become tied to economic/social possibilities or consequences. ▪ Navigation of subjectivities (gov. levels, demographic groups, etc.) towards standard ID form ▪ Similar systems of thought; Foucault’s governmentality & “population as datum,” and Hobbesian logic. ▪ Can we see similarities between the SSN and IDNYC? How can we interpret the statement “IDNYC…gives all of us the opportunity to show who we are—New Yorkers” (NYC, 2019)? ▪ How is “New Yorker” an identity? What is the standard “New Yorker”? ▪ Who determines what should be standardized and the process? These readings allowed me to really start thinking critically about how standards are truly embedded in society, buy why? and how are they embedded? Who and how are they governed and enforced? ▪ Question for the class: How are biometric data collected by the Canadian government stored? ▪ Should citizens have a right to “opt-in” or “opt-out” when providing their biometric data? ▪ How does the OPS collect / use these data? ▪ Where do collected data get sent and who transforms them into a biometric fact? ▪ Using Mork Lomell’s (2011) understanding of police annual reports as performative and persuasive which present an idealized image of the institution, in what ways can we descriptively map the Ottawa Police Service’s annual report? ▪ Do standards have power? What do they afford? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  53. 53. Week 8 - Thematic Materials http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  54. 54. 4. Indigenous knowledge and communication infrastructure in-class mapping Assignment Week 8 Mar. 4 (10%) ▪In the Map, Data and Government Information Centre there is a map display entitled the Evolution of the Communication Infrastructure in Canada with some maps about Aboriginal People in Canada. The maps are organized into groups, you will be assigned a set of maps and will be provided with an in-class assignment. You will be required to consider the Harley paper and the Phillips keynote. ▪Look at authors, names, titles, table of contents, the placement of things, vocabulary, labels, data contributors, relative importance, etc. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  55. 55. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  56. 56. MacOdrum Library Atlas of Canada & Canada Year Book Display http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  57. 57. Week 8 – Reading Reflection Questions ▪ Using Phillips’ notion and understanding of the problem of standards, how can we understand “dipstick” data and the problem of big data and how sporadic research we’ve examined in our class readings? Why does Phillip’s stress the notion for understanding the context of data? ▪ do cartographers have similar third-party financing/market/licensing considerations? ▪ Who makes use of maps, who owns them, and is this different than who creates them? ▪ How might this raise similar concerns as in Scassa? In Scassa, crime mapping is envisioned as a tool of “civil engagement” – do maps have these same goals? How might this differ with a “scientific” map vs. a national map? ▪ Is engagement similar or different in crime vs. maps? We can also consider paper vs. digital mapping – does digital mapping allow more engagement? ▪ If street-level officers are resistant to knowledge and procedural changes that conflict with ingrained training principles and front line practice, how could police services work to effectively introduce new technological and data-oriented practices into operations http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  58. 58. Week 9 – Big Data http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  59. 59. Week 9 – Thematic Material http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  60. 60. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  61. 61. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  62. 62. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  63. 63. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  64. 64. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  65. 65. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 DRAFT
  66. 66. Week 9 – Reading Reflection Questions ▪Does understanding the internal problems of police using big data (i.e. costs, which would be increased exponentially by the implementation of wearable technology) abate some of our concerns over privacy and surveillance? ▪A lot of work has been done to identify the current issues with corporate surveillance in the online world, but what can be done to protect consumers? ▪Should there be a governing body that oversees the actions of these data brokers, to ensure that the consumers best interests are protected? ▪Do consumers have the right to request the “dossier” of information that major platforms such as Facebook and Google have collected on them? ▪Who is the true owner of these data? ▪After our visit to the OPS, are they ready to shift towards a big data environment? ▪What is the discursive regime of the OPS? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  67. 67. Week 10 – Probability & Risk http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  68. 68. Week 10 – Thematic Material http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  69. 69. Local and Traditional Knowledge http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  70. 70. Licencing LTK http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  71. 71. Week 10 – Reading Reflection Questions ▪ Would the use of predictive analytics make the OPS’ operations more evidence-based as Mears suggests? ▪ Are there any other examples (OPS or otherwise) of classification systems reflecting social, political, religious, and/or cultural beliefs (Crawford, 2017) of the group constructing it? ▪ The US Department for Justice states that federal law enforcement may consider race in the effort of protecting national security (US Department of Justice, 2003, cited from, Guzik, 2009). With this in mind, how can we overcome racial biases in society when they are embedded in society from legislation to design in algorithms? ▪ How can we ensure that the design of the algorithms do not have racial biases from the get go? ▪ Considering Schlehahan et al and Mentello’s considerations surrounding pre-emptive policing and the issues with surveillance, do you think Ottawa Police Services should engage with predictive policing? ▪ Are there any particular considerations that should be raised because of Ottawa is Canada’s nation-state capital? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  72. 72. Week 11 - Infrastructure http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  73. 73. Week 11 – Thematic Material http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  74. 74. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  75. 75. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  76. 76. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  77. 77. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  78. 78. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  79. 79. Final Abstract & Poster http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  80. 80. Data Day 6.0 http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  81. 81. Data Day 6.0 http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  82. 82. Data Day 6.0 Program http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  83. 83. Week 11 – Reading Reflection Questions ▪What kind of infrastructural improvements would be most beneficial to the OPS? ▪Can smart city technology be used to inform the OPS in terms of predictive policing (a theme from last weeks readings)? ▪What kind of big data analysis can be done with data collected through smart city technologies such as IoT and could those be used for surveillance purposes? Who owns these data? How can they be governed? ▪Pallito offers a notion toward a normative framework for analyzing bargains involving privacy in which the “first-order duty is to the self” in which we ought to value our own privacy. Do you agree with the concluding chapter or are there real concrete actions that are missing? What would you add? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  84. 84. Week 12 - From Critical Theory to Action http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  85. 85. Week 12 – Thematic Materials http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  86. 86. Week 12 – Reading Reflection Questions ▪Using GDP as a measurement of national wellbeing is required to be a member of the United Nations, which (while itself is a problematic organization) brings with it access to assistance from the World Bank and International Monetary Fund. However, GDP is not an accurate measure of how people are doing. How can we even start to go about reorganizing worldviews so that GDP is pushed to the side like a relic, allowing us to make more nuanced assessments about ourselves and our neighbours (both locally and across the globe)? ▪How has the critical theory learned in this class changed (or perhaps has not) your perspective on data and the OPS? ▪Does the phrase “technology can be used for bad or for good” (from Do Not Resist) imply that technology is initially constructed neutral (and then used well/poorly)? ▪What would be a better way to think about the possibilities and consequences of technology? ▪Can/should we even use terms like “good” and “bad”? http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  87. 87. Week 13 - Assemblages, Genealogies and Dynamic Nominalism “data are commonly understood to be the raw material produced by abstracting the world into categories, measures and other representational forms – numbers, characters, symbols, images, sounds, electromagnetic waves, bits – that constitute the building blocks from which information and knowledge are created” (Kitchin, 2014:1) “data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyze them” (Kitchin, 2014:2) http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  88. 88. 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 Critical Social Science Science Technology Studies 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. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  89. 89. Dynamic NominalismModified from Ian Hacking’s Dynamic Nominalism Tracey P. Lauriault, 2012, http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  90. 90. Social-shaping qualities of data Kitchin, 2012, Programmable City http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  91. 91. Critical Data Studies Research and thinking that applies critical social theory to data to explore the ways in which they are never ▪ simply neutral, ▪ objective, ▪ independent, ▪ raw representations of the world, Data are instead understood to be: ▪ situated, ▪ contingent, ▪ relational, ▪ contextual, and ▪ do active work in the world. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Kitchin and Lauriault, 2015
  92. 92. Critical Data Studies Vision ▪Unpack the complex assemblages that produce, circulate, share/sell and utilise data in diverse ways; ▪Chart the diverse work they do and their consequences for how the world is known, governed and lived-in; ▪Survey the wider landscape of data assemblages and how they interact to form intersecting data products, services and markets and shape policy and regulation. Kitchin and Lauriault, 2015 http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  93. 93. 7 provocations 1. Situate data regimes in time and space 2. Expose data as inherently political and whose interests they serve 3. Unpack the complex, non-deterministic relationship between data and society 4. Illustrate the ways in which data are never raw 5. Expose the fallacies that data can speak for themselves and that big data will replace small data 6. Explore how new data regimes can be used in socially progressive ways 7. Examine how academia engages with new data regimes and the opportunities of such engagement. Craig Dalton and Jim Thatcher, 2014 Image Source: Economic Times, Indicators page, 2013 http://articles.economictimes.indiatimes.com/2013-03-13/news/37683866_1_trade-data- interstate-trade-inter-state-trade http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  94. 94. http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Data Indicators Facts Classification Institutions Standards Maps Big Data Prediction Infrastructure Producing Knowledge Action
  95. 95. Critically Engaged Pedagogy http://doi.org/10.22215/tplauriault.courses.2019.coms5225 Topic Theory Domain Theory Domain Praxis OPS Site Visit & Interview Grey Literature & Data Crime, Safety, Policing, etc. Critical Data Studies
  96. 96. Assignments http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  97. 97. 2. Weekly 1-2-page (max) Reading Reflections (30%) submit 6 / 11 Weeks by 10AM day of class ▪Students are asked to submit weekly critical reflections of a combination of a set of readings, thematic readings and thematic encyclopaedia readings. ▪Students will conceptually integrate the material for that week and will identify concepts that may inform their paper and/or poster project. The reflection should end with a question for the class. http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  98. 98. 3. Research Paper & Poster Ottawa Police Service Data Assemblage 50% Students will demonstrate their familiarity with the course material by applying critical data studies concepts and theories related to this year’s theme which is Ottawa Police Services assemblage of data, indicators, maps, crime statistics and governance. In this assignment students are to research and map out the socio-technological data assemblage of the Ottawa Police in a poster project. The poster will be submitted at the Data Day 6.0 Conference on March 26 organized by the Carleton Institute for Data Science. Students will also write a 15-page research paper about this assemblage, and are asked to ontologically and epistemologically consider the Ottawa Police Services data processes? What currently frames the data collection approach? If that framing changes, what would change? Is there a data governance plan in place? What else could be collected and why? Are there any biases? Is this a data driven institution? Is the public adequately informed? 3.1 Field Trip to the downtown Ottawa Police Communication Centre Week 4, Jan. 28 3.2 Ottawa Police Data Assemblage Brain Storm, Week 5, Feb. 4 (5%) ▪ Students can use Mindmap, Coggle.it, or power point or any other tool to draw out/illustrate anything related to the socio- technological assemblage of the data and data system encountered during the field trip. Student will share their observations for 5 minutes each in class the following week. 3.3 Poster Project Proposal, 1-page Quad Chart, Week 6 Feb.11 (5%) ▪ Introduce what you will examine ▪ Provide two potential research questions ▪ State your methodological approach ▪ References http://doi.org/10.22215/tplauriault.courses.2019.coms5225
  99. 99. 3. Ctnd. 3.4 DRAFT Outline of the paper and poster abstract for peer review Week 7, Feb. 25 ▪ Follow the CUIDS instructions. 3.5 Submit Final Poster Abstract to CUIDS date TBD (5%) 3.6 Digital Draft of Poster for In-Class Peer Review Week 9 Mar. 11, in class (5%) ▪ See CUIDS instructions. Note that a poster is a form of scholarly communication common in science and engineering. Keep in mind that your poster will be somewhat different, and you will adapt it to critical data studies and your topic. This is not an infographic. Here are some useful guidelines: ▪ NYU Libraries Guide: http://guides.nyu.edu/c.php?g=276826&p=1846154 ▪ Urbana Champaign Library Guide: http://guides.library.illinois.edu/c.php?g=347412&p=2343433 ▪ 10 Simple Rules for a Good Poster Presentation: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1876493/ 3.7 Print poster and submit digital copy to CULearn Week 10 Mar. 20 (15%) ▪ If your poster is accepted for Data Day 6.0 a print out of your poster will be required and generally there is a cost to this (+/- 40$). Should your poster not be accepted a digital copy only is to be submitted. Whether or not your poster is accepted does not affect your mark. 3.9 Submit a draft paper for peer review Week 11, March 25 3.8 Attend Data Day 6.0 Poster Session Week 11 on Tuesday Mar. 26 3.10 Submit final paper to CULearn Week 13 April 8, 20%. A copy of the paper and poster will also be shared with Cameron Hopgood, Manager, Business Performance Unit at the Ottawa Police Service. http://doi.org/10.22215/tplauriault.courses.2019.coms5225

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