COMS 5225 - Critical Data Studies (Winter 2018)

Communication Studies, Carleton University

Class Schedule: Fridays, 8:30 - 11:30

Location: Residence Commons 212

Instructor: Dr. Tracey P. Lauriault

E-Mail: Tracey.Lauriault@carleton.ca include (COMM 5225 in the subject line)

Office: 4110 River Building

Office Hours: Tuesdays 13:00-16:00, Fridays after 1pm (by appointment)

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Course Description and Objectives
Assignments
Readings and Schedule

Course Description

The emphasis of this class is to learn to envision data genealogically, as assemblages, as part of a dispositif and infrastructure in order to reframe them beyond technological conceptions. Emphasis will be on the sociotechnical, political and philosophical aspects of data. 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; and the circularity of the transduction and translation of people, space and social phenomena into and by data and software and finally we will discuss the role of data in the production of knowledge.

The class will be led like a workshop, and we will both discuss and apply theory. The theme for this term will be dashboards and control rooms with the City of Ottawa Paramedic Communication Centre (Despatch) as the case study.

Assignments

Assignment	Weight
Reportage of Readings	15%
Data Description and Conceptualization Assignment	10%
Field Trip Paramedic Communication Centre	5%
Communication Centre Assemblage Brain Storm	5%
Poster Project Proposal	5%
Poster Abstract Draft 1 & Peer Review	5%
Submit Poster Abstract (CuLearn and CUIDS)	5%
In-Class Map Assignment	10%
Draft of Poster for In-Class Peer Review	5%
Print Poster and Submit to CuLearn	20%
Attend Data Day 5.0	5%
Submit Paramedic Dashboard Report	10%
Total	100%

General Assignment submission instructions:

• Submit to cuLearn

• Format: .doc, .docx, .rtf (NOT .pdf NOT .Pages)

• Use 12 pt font, 1.5 line spacing, 1-inch margins and indent paragraphs.

• Include page numbers

• Citation style: Chicago, Harvard, APA

• Include a document header as follows:

COMS5225A Communication, Technology and Culture, Submitted to: Dr. Tracey P. Lauriault, Assignment #, dd/mm/yyyy, First and Last Name, Student ID

• File naming convention:

LastNameFirstName_COMS5225_Assignment#

Example: OrganaLeia_COMS5225_PaperProposal.docx

Reading Reportage (25%):

A paper reportage schedule will be made during the Week 1 class in collaboration with students. The students will lead a 1 hour seminar based on the assigned readings. This will include a 40-45 minute lecture and the facilitation of a 15-20 minute for Q & A session. Students are encouraged to augment the reading with related real world examples based on real world data.

Data Description and Conceptualization- Due Week 2 11:55 Jan. 17 (10%):

Select a dataset directly related to a topic or a subject you might engage with or are currently engaged with in your graduate work or a dataset in the wild.

In a total of 3 pages describe them 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, include licences and terms of use, how are they disseminated, the producing institution, data authors if there are any, who created them, dates, geography, classifications, models, methods, and etc. Be sure to cite the dataset & provide the URL, be sure to cite any other documentation related to this dataset, you can use footnotes if you like, but use full citation. Get to know these data and familiarize yourself with them.

You will also conceptually frame these data by applying at least 2 of Kitchin's conceptualizations beyond the technical and see if you can identify any elements of the socio-technological assemblage. You might also want to say a little something about your interest in this dataset, what you might use the data for, how the data are conventionally used and explain what led you to trust these data. You are welcome to use screen-captures and they will not go against your page count.

Think of this as a critically informed lab report.

Poster Project (8 parts):

Students will demonstrate their familiarity with the course material by applying critical data studies concepts and theories in relation to the data studied during the Paramedic Communication Centre field trip. This can be a specific dataset, database, algorithm, data infrastructure, data system, control room, dashboard, standards and etc. of their choice and this will be communicated in a poster format and presented at Data Day 5.0 on March 27 organized by the Institute for Data Science.

3.1 Field Trip to the Paramedic Communication Centre Week 3 Jan. 26. (5%)

Logistic details to follow.

3.2 Paramedic Control Room Data Assemblage Brains Storm, Week 4, Feb. 2 (5%)

Students can use Mindmap, Coggle.it, or power point or any other tool to draw out/illustrate anything they like related to the socio-technological assemblage of the data or data system encountered at the Paramedic Communication Centre. Students will submit this to CULearn and will discuss their ideas for 5 minutes each in class. We will then allocate time to discuss these ideas as a group in class.

3.3 Poster Project Proposal, Week 5 Feb.9, (5%), 1 page Quad Chart:

1. Introduce the dataset, database, indicator, data infrastructure, data standard you will discuss in your poster.
2. Provide two potential research questions.
3. State which theory, philosophy and concepts you plan to engage with & why.
4. References.

3.4 DRAFT Poster Project Abstract for peer review Week 6 Feb. 16 (5%):

See CUIDS instructions.

3.5 Submit Final Poster Abstract Week 7, Mar. 2 (5%)

3.6 Digital Draft of Poster for In-Class Peer Review Week 9 Mar. 16, in class (5%)

See CUIDS instructions. Note that a poster as a form of scholarly communication is common in scientific fields, geomatics and engineering. The following are some general instructions to guide the production of your posters but keep in mind that your poster will be somewhat different and you will have to adapt these techniques to critical data studies and your topic. Also, note this is not an infographic, this is a ‘scientific’ poster. Here are some useful guidelines:

• NYU Libraries Guide
• Urbana Champaign Library Guide
• 10 Simple Rules for a Good Poster Presentation

3.7 Print poster and submit digital copy to CULearn Week 10 of Mar. 20, 20%

If your poster is accepted for Data Day 4.0 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.

3.8 Attend Data day 5.0 Poster Session Mar. 27.

4. Paramedic Dashboard Feedback report Week 11 Apr. 6 (10%)

Late Policy:

Extensions must be requested in advance, in person, and will typically require documentation of an extended illness or other significant disruption to your ability to complete the required work.

Readings & Schedule

Week	Theme
Week 1 (Jan.12)	Introduction – Data Stories and Conceptualizing data?
Week 2 (Jan.19)	Assemblages, Dashboards & Control Rooms
Week 3 (Jan.26)	Paramedic Communication Centre Field Trip, no readings
Week 4 (Feb.2)	Facts & Data
Week 5 (Feb.9)	Indicators & Categorization
Week 6 (Feb.16)	Administrative and Survey Data
Winter Break (Feb.19 – 23)	No classes
Week 7 (Mar.2)	Standards
Week 8 (Mar.9)	Mapping
Week 9 (Mar.16)	Big Data
Week 10 (Mar.23)	Probability & Risk
Data Day (Mar.27)	No classes
Week 11 (Apr.6)	Data Infrastructure
Week 12 (Apr.11)	Assemblages, Genealogies and Dynamic Nominalism

Week 1 (Jan.12) – Introduction – Data Stories and Conceptualizing data

Welcome to the class! We will get to know each other, exchange some data stories, and critically read some datasets and go over the course outline.

Class will be informed by:

• Kitchin, Rob, (2014) Conceptualizing Data, Chapter 1 The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences, Sage: UK.

• Bell, Genevieve, (2015) The Secret Life of Big Data, in Boellstroff, Tom and Maurer, Bill, eds. Data, Now Bigger and Better! Chicago: Prickly Paradigm Press.

Week 2 (Jan.19) – Assemblages, Dashboards & Control Rooms

This week we prepare for the Paramedic Communication Centre field trip and we critically discuss related literature.

All Read:

• Kitchin, Rob; Lauriault, Tracey P. and McArdle, Gavin (2014) Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards, Regional Studies and Regional Science

• Kitchin, Rob and Lauriault, Tracey P. (2014) Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work. The Programmable City Working Paper 2; pre-print version of chapter to be published in Eckert, J., Shears, A. and Thatcher, J. (eds) Geoweb and Big Data. University of Nebraska Press.

• Mattern, Shannon (2015) Mission Control: A History of the Urban Dashboard, Places Journal.

• Tkack, Nathaniel & Bartlett, Jamie, Governance by Dashboard, (2017) A Policy Paper

Ottawa Paramedic Communication Centre Material:

• About Paramedic Services

• Community and Protective Services Committee Comité des services communautaires et de protection Report

• The Canadian CBRNE Paramedic Training Competency Profile and Best Practices June 2011

• EMS1.com Guide to Emergency Technology

• Technology Innovation and Economics Policing Plan Report

• Paramedic Chiefs Digital Edition, not available online.

Reference material:

• Reading Dashboards Blog
• Brian Hunt, Toni Ivergard eds. (2008) Handbook of Control Room Design and Ergonomics: A perspective for the Future, 2nd Edition CRC Press (PDF)

Week 4 (Feb.2) – Facts and Data

This week we discuss objectivity and the production of facts and data from multiple perspective and with different theoretical approaches.

All Read:

• Gruber Garvery, Ellen, (2013) “facts and FACTS”: Abolitionists’ Database Innovations, In Gitelman, L. (ed) “Raw Data” is an Oxymoron. MIT Press, Cambridge, pp 89-103.

• Porter, Theodore M., (2003) Measurement, Objectivity, and Trust, Measurement Interdisciplinary Research and Perspectives, 1:4, 241-255

• Ribes, D. and Jackson, S.J. (2013) Data bite man: The work of sustaining long-term study. In Gitelman, L. (ed) “Raw Data” is an Oxymoron. MIT Press, Cambridge, pp 147-166.

• Rosemberg, Daniel, (2013) Data Before the Fact, In Gitelman, L. (ed) “Raw Data” is an Oxymoron. MIT Press, Cambridge, pp.15-41.

Pick one of the following:

• Campbell, Rebecca; Shaw, Jessica and Fehler–Cabral, Giannina (2015) Shelving Justice: The Discovery of Thousands of Untested Rape Kits in Detroit, City & Community, 14 (2) 2, pp.151–166.

• Comas-d’Argemir, Dolors, (2015) News of Partner Femicides: The Shift from Private Issue to Public Problem. European Journal of Communication, 30(2), pp. 121-137.

Week 5 (Feb.9) – Indicators & Categorization

Humans like to make sense of the world by sorting things out into classifications and then measure them with indicators. This week we examine the classic Hacking’s social constructivist view of classifying and how classification is key to infrastructural thinking.

All read:

• Bowker, Geoffrey C. and Leigh Star, Susan (2002) Categorical Work and Boundary Infrastructures: Enriching Theories of Classification, Chapter 9 in Sorting Things Out: Classification and its Consequences, p.285-317.

• Crawford, Kate (2017) The Trouble with Bias - NIPS 2017 Keynote- Kate Crawford #NIPS2017.

• Hacking, Ian, 1986, Making Up People, in Reconstructing Individualism, ed., T. Heller et al, Stanford, Calif.: Stanford University Press, pp. 222-236.

• Hacking, Ian, 1991, A Tradition of Natural Kinds, Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, Vol. 61, No. 1/2, The Twenty-Ninth Oberlin Colloquium in Philosophy, Feb., pp. 109-126.

• Philipsen, Dirk, (2015) Born from Disaster: The Making of Key Measure, Chapter 4 in The Little Big Number How GDP Came to Rule the World, New Jersey: Princeton University Press. pp.83-107.

• Zuberi, Tukufu (2001) The Evolution of Racial Classification & Deracializing the Logic of Social Statistics Chapters 1 & 7 in Thicker Than Blood: How Racial Statistics Lie, University of Minnesota Press. pp.17-27, 123-145.

Reference Material:

Moonen, T., Clark, G. Couturier (2015) The Business of Cities 2015: What do 150 city indexes and benchmarking studies tell us about the urban world in 2015? Jones Lang LaSalle.

Week 6 (Feb. 16) – Administrative and Survey Data

All Read:

• Desrosieres, Alain (1998) Introduction: Arguing from Social Facts in The Politics of Large Numbers: A History of Statistical Reasoning. Cambridge: Harvard University Press. pp.1-16.

• Foucault, Michel, Governmentality, in Faubion, James D. Ed. (1994) Power, New York: The New Press, pp.201-222.

• Marks, John, 2008, Michel Foucault: Biopolitics and Biology, Chapter 4 in Morton, Stephen and Stephen Bygrave, eds. 2008, Foucault in an Age of Terror: Essays on Biopolitics and the Defence of Society, New York, Palgrave Macmillan, pp. 88-104.

• Starr, Paul and Corson, Ross (1989) Who will have the Numbers? The Rise of the Statistical Services Industry and the Politics of Public Data, Chapter 14 in Alonson, William and Starr, Paul (Eds) The Politics of Numbers, New York: Russel Sage Foundation, pp. 415-447.

Reference Material:

• UN Big Data Project Inventory

• Statistics Canada, Directive of Record Linkages

• Justice Data Lab

• Policy Wise for Children and Families

• Powered by Data, How a social innovation is unlocking government administrative data

Week 7 (Mar. 2) – Standards

Standards and interoperability are the bread and butter of data infrastructures and cloud computing and very little gets done without them. This week we examine the control and power exerted by these unsung technocratic heros!

All Read:

• Florence Miller, Metadata Standards: Trajectories and Enactment in the Life of an Ontology, in Standards and their stories: how quantifying, classifying, and formalizing practices shape everyday life, Ithaca: Cornell University Press, pp.149-177.

• Lampland, Martha, and Star, Susan Leigh, (2009) Reckoning with Standards , Standards and their stories: how quantifying, classifying, and formalizing practices shape everyday life, Ithaca: Cornell University Press, pp.3-35

• Wilks, Yorick, (2014) Beyond the Internet and the Web, Chapter 23 in Graham, Mark and Dutton, William H. Society and the Internet: How Networks of Information are Changing Our Lives, Oxford: Oxford University Press, pp.360-375.

Reference Material:

• T3010

• Ajah

• Open Corporates

• IATI

• DOI

• ORCID

• ISO

Week 8 (Mar. 9) – Mapping

This class will take place in the library where we examine large technological systems and the materiality of infrastructure by studying the curated map, atlas and document display entitled the the Evolution of the Canadian Communication Infrastructure You will learn to read communication technology maps, identify patterns, indicators, the geography of communication, and consider policy, politics, business and economics as these pertain to communication and nation building.

All Read:

• Harley, J. B. (1989). Deconstructing the Map. Cartographica, 26 (2), pp.1-20.

• Kitchin, Rob; Lauriault, Tracey and Wilson, Matt (2017) Chapter 1, Understanding Spatial Media, Sage: London.

• Peluso, N.L (1995). Whose Woods are These? Counter-Mapping Forest Territories in Kalimantan, Indonesia. Antipode. 4. 27: 383–406.

• Sparke, Matthew (1998) A Map that Roared and an Original Atlas: Canada, Cartography, and the Narration of Nation, Annals of the Association of American Geographers, Volume 88, Issue 3:463–495.

Reference Material:

• Dodge, Martin and Rob Kitchin (2001) The Atlas of CyberspaceChapters 1 Mapping Cyberspace & 2 Mapping Infrastructure and Traffic, pages 10-22, 52-55.

• Government of Canada Report: The Canadian Geospatial Data Infrastructure vision, mission and roadmap - The way forward.Natural Resources Canada, Information Product 28e, 2012; 20 pages, Also available here.

• Ordnance Survey Ireland (OSi) (2016) Statement of Strategy 2016-2018.

In-Class Map Assignment 10%.

In the Map, Data and Government Information Centre there is a map display entitled the Evolution of the Communication Infrastructure in Canada. The maps are organized into groups and will be assigned a set of maps and an in-class assignment will be handed to you. You will be required to consider the Harley Paper for this assignment. These maps and books are irreplaceable please treat them with care.

You will see a number of manuscripts from the Canada Year Books (1861-2011) as well as three Commissions (1929, 1949-51 & 1957) in the Library. Six of the Year Books include a special history of communications in Canada (1932, 1933, 1947, 1957-58, 1959 and 1967). You are welcome to refer to these for your in-class assignment.

Week 9 (Mar. 16) – Big Data

Are big data the end of science? Are they everything or nothing? Are they just about controlling the future with numbers? Is it corporate hype?

All Read:

• Verhoef, Peter C.; Kooge, Edwin and Walk, Natasha (2016) Data Data Everywhere, Chapter 3 in Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Milton Park: Routledge, 75-93.

• Pasquale, Frank, (2015) Digital Reputation in the Era of Run Away Big Data, The Black Box Society: The Secret Algoritms that Control Money and Information, Cambridge MA: Cambridge University Press, 19-59.

• Kitchin, Rob. (2014), Chapter 5 Enablers and Sources of Big Data, pp. 80-89. The Data Revolution. London: Sage.

• Kitchin, R. and McArdle, G. (2016) What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets, Big Data and Society.

Reference Material:

• Government of Ireland: Assessing the Demand for Big Data and Analytics Skills, 2013 – 2020 Report.

• Canada’s Big Data Consortium (2014) Closing Canada’s Big Data Talent Gap. No longer available online.

• McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity.

• Canadian Internet Public Policy Interest Clinic (2016) On the Data Trail: How detailed information about you gets into the hands of organizations with whom you have no relationship.

Week 10 (Mar. 23) – Probability and Risk

Some suggest that the era of big data is the era of probability revisited. We will look at Hacking’s work on the Taming of Change which is an historical account of the moment when probably entered our culture and we will look at contemporary applications.

All Read:

• Hacking, Ian, (1990) Chapter 1 The Argument and Chapter 23 The Universe of Chance in The Taming of Chance, Cambridge: Cambridge University Press, pp.1-10 & pp.200-216.

• Guzic, Keith, (2009) Discrimination by Design, Predictive Data Mining as Security Practice in the United States ‘War on Terrorism’, in Surveillance Systems, 7(1) pp. 1-20.

• Mantello, Peter (2016) The machine that ate bad people: The ontopolitics of the precrime assemblage, Big Data & Society 3(2).

• Marron, Donncha (2007) Lending by Numbers’: Credit Scoring and the Constitution of Risk within American Consumer Credit, Economy and Society, 36(1) pp.103-133.

• Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai (2016) Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings, NIPS.

Reference Material:

• Perry, Walter L.; McInnis, Brian; Price, Carter C.; Smith, Susan C.; and Hollywood, John S. (2013) Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations; Washington D.C.: The RAND Corporation, pp. xxiii-xxiv and 1-15.

Week 11 (Apr.6)- Data Infrastructure

We have look at aspect of data infrastructure throughout the class and this week we look at these large technological system philosophically and pragmatically.

All Read:

• Dourish, Paul and Genevieve Bell, 2007, The Infrastructure of Experience and the Experience of Infrastructure: Meaning and Structure in Everyday Encounters with Space, Environment and Planning B: Planning and Design, Volume 34, pp. 414-430.

• Edwards, Paul N., Steven J. Jackson, Geoffrey C. Bowker and Cory P. Knobel, 2007, Understanding Infrastructures: Dynamics, Tensions and Design, Report of a Workshop on History & Theory of Infrastructure: Lessons for New Scientific Cyberinfrastructures, US National Science Foundation, accessed June 22, 2008.

• Latour, Bruno (1987) Centres of Calculation, Science in Action. Cambridge: Harvard University Press.215-258.

• Pulsifer, Peter L., Taylor, D. R. F. 2007, Spatial Data Infrastructure: Implications for Sovereignty in the Canadian Arctic, in the Canadian Polar Commission newsletter, Meridian, spring-summer, April 25, pp. 1-5.

Reference Material:

• MASSTLC Big Data Cluster

• MASSTech Big Data Landscape

Week 12 (Apr. 11) – Assemblages, Genealogies and Dynamic Nominalism

No readings are assigned for this week. In-class we will examine three methodological approaches that can be applied to the study of data big and small. Examples will be drawn from the professor’s first-hand experience in three cities and three different case studies. In addition we will review the theories discussed throughout the term.

COMS 5225 Syllabus by Tracey P. Lauriault is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://github.com/TraceyLauriault/COMS5225_Winter2018