1887
Volume 23 Number 1
  • ISSN 0142-5471
  • E-ISSN: 1569-979X

Abstract

Working with data is an increasingly powerful way of making knowledge claims about the world. There is, however, a growing gap between those who can work effectively with data and those who cannot. Because it is state and corporate actors who possess the resources to collect, store and analyze data, individuals (e.g., citizens, community members, professionals) are more likely to be the subjects of data than to use data for civic purposes. There is a strong case to be made for cultivating data literacy for people in non-technical fields as one way of bridging this gap. Literacy, following the model of popular education proposed by Paulo Freire, requires not only the acquisition of technical skills but also the emancipation achieved through the literacy process. This article proposes the term creative data literacy to refer to the fact that non-technical learners may need pathways towards data which do not come from technical fields. Here I offer five tactics to cultivate creative data literacy for empowerment. They are grounded in my experience as a data literacy researcher, educator and software developer. Each tactic is explained and introduced with examples. I assert that working towards creative data literacy is not only the work of educators but also of data creators, data publishers, tool developers, tool and visualization designers, tutorial authors, government, community organizers and artists.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 license. For any use beyond this license, please contact the publisher at [email protected]
Loading

Article metrics loading...

/content/journals/10.1075/idj.23.1.03dig
2017-01-01
2019-10-23
Loading full text...

Full text loading...

References

  1. Andrejevic, M
    (2014) Big Data, Big Questions: The big data divide. International Journal of Communication, 8, 1673–1689.
    [Google Scholar]
  2. Bardzell, S
    (2010) Feminist HCI: Taking Stock and Outlining an Agenda for Design. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp.1301–1310). New York, NY, USA: ACM. doi: 10.1145/1753326.1753521
    https://doi.org/10.1145/1753326.1753521 [Google Scholar]
  3. Bhargava, R
    (2014) Speaking Data – Data Therapy. RetrievedOctober 7, 2016, fromhttps://datatherapy.org/2014/07/09/speaking-data/
  4. Bhargava, R. , Kadouaki, R. , Castro, G. , Bhargava, E. & D’Ignazio, C
    (2016) Data Murals: Using the Arts to Build Data Literacy. Journal of Community Informatics, 12. Retrieved fromci-journal.net/index.php/ciej
    [Google Scholar]
  5. Boyd, Danah & Crawford, K
    (2012) Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679. doi: 10.1080/1369118X.2012.678878
    https://doi.org/10.1080/1369118X.2012.678878 [Google Scholar]
  6. Crawford, K
    (2016, June25). Artificial Intelligence’s White Guy Problem. The New York Times. Retrieved fromwww.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html
    [Google Scholar]
  7. Crawford, K. , Gray, M.L. & Miltner, K
    (2014) Big Data| Critiquing Big Data: Politics, Ethics, Epistemology| Special Section Introduction. International Journal of Communication, 8, 10.
    [Google Scholar]
  8. Dalton, C.M. , Taylor, L. & Thatcher, J
    (2016) Critical Data Studies: A Dialog on Data and Space (SSRN Scholarly Paper No. ID 2761166). Rochester, NY: Social Science Research Network. Retrieved frompapers.ssrn.com/abstract=2761166
    [Google Scholar]
  9. Diakopoulos, N
    (2015) Algorithmic Accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 1–18. doi: 10.1080/21670811.2014.976411
    https://doi.org/10.1080/21670811.2014.976411 [Google Scholar]
  10. D’Ignazio, C. & Bhargava, R
    (2015) Approaches to Building Big Data Literacy. Bloomberg Data for Social Good. Retrieved fromwww.kanarinka.com/wp-content/uploads/​2015/​07/​Big_​Data_​Literacy.pdf
    [Google Scholar]
  11. (2016) DataBasic: Design Principles, Tools and Activities for Data Literacy Learners. The Journal Of Community Informatics, 12(3). Retrieved fromwww.ci-journal.net/index.php/ciej/article/view/1294
    [Google Scholar]
  12. DiMaggio, P. , Hargittai, E
    . & others (2001) From the “digital divide”to “digital inequality”: Studying Internet use as penetration increases. Princeton: Center for Arts and Cultural Policy Studies, Woodrow Wilson School, Princeton University, 4(1), 4–2.
    [Google Scholar]
  13. Blackburn-Dwyer, B
    (2016) This Necklace Shows Just How Clean (or Dirty) Your Air Is. RetrievedDecember 30, 2016, fromhttps://www.globalcitizen.org/en/content/pollution-necklace-jewelry-wearable-tech/
  14. Emerson, J Tactical Technology Collective
    & (2013) Visualizing information for advocacy. Bangalore, India: Tactical Technology Collective.
    [Google Scholar]
  15. Freire, P
    (1968) Pedagogy of the oppressed. New York: Continuum.
    [Google Scholar]
  16. Gray, J. , Bounegru, L. , Chambers, L European Journalism Centre & Open Knowledge Foundation
    (2012) The data journalism handbook: how journalists can use data to improve news. Sebastopol, CA: O’Reilly Media.
    [Google Scholar]
  17. Goldsmith, S. & Crawford, S
    (2014) The Responsive City: Engaging communities through data-smart governance. John Wiley & Sons.
    [Google Scholar]
  18. Harris, J
    (2012, September13). Data Is Useless Without the Skills to Analyze It. RetrievedSeptember 12, 2016, fromhttps://hbr.org/2012/09/data-is-useless-without-the-skills
  19. Huron, S. , Carpendale, S. , Thudt, A. , Tang, A. & Mauerer, M
    (2014) Constructive visualization. In Proceedings of the 2014 conference on Designing interactive systems—DIS ’14 (pp.433–442). New York, New York, USA: ACM Press. doi: 10.1145/2598510.2598566
    https://doi.org/10.1145/2598510.2598566 [Google Scholar]
  20. Jacob, N
    (2016) Boston Smart City Playbook — from the Mayor’s Office of New Urban Mechanics. RetrievedSeptember 15, 2016, fromhttps://monum.github.io/playbook/
  21. Keller, E.F
    (1996) Reflections on Gender and Science: Tenth Anniversary Paperback Edition (Anniversary). New Haven: Yale University Press.
    [Google Scholar]
  22. Julia Angwin , Surya Mattu , Jeff Larson , & Lauren Kirchner
    (2016, May23). Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks. RetrievedSeptember 12, 2016, fromhttps://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  23. Landström, C
    (2007) Queering feminist technology studies. Feminist Theory, 8(1), 7–26. doi: 10.1177/​1464700107074193
    https://doi.org/10.1177/​1464700107074193 [Google Scholar]
  24. Letouzé, E
    (2015) “Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data.”Retrieved fromdatapopalliance.org/item/beyond-data-literacy-reinventing-community-engagement-and-empowerment-in-the-age-of-data/
    [Google Scholar]
  25. Lohr, S
    (2014, August18). For Big-Data Scientists, “Janitor Work” Is Key Hurdle to Insights. New York Times.
    [Google Scholar]
  26. Lupi, G. , Posavec, S. & Popova, M
    (2016) Dear data. New York: Princeton Architectural Press.
    [Google Scholar]
  27. Maine Data Literacy Project
    . (n.d.). RetrievedSeptember 12, 2016, fromparticipatoryscience.org/project/maine-data-literacy-project
  28. Maycotte, H.O
    . (n.d.). Data Literacy—What It Is And Why None of Us Have It. RetrievedSeptember 12, 2016, fromwww.forbes.com/sites/homaycotte/2014/10/28/data-literacy-what-it-is-and-why-none-of-us-have-it/
  29. Mayer-Schönberger, V. & Cukier, K
    (2013) Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.
    [Google Scholar]
  30. Merriam, S.B
    (1998) Qualitative Research and Case Study Applications in Education. Revised and Expanded from “Case Study Research in Education”. Jossey-Bass Publishers, 350 Sansome St, San Francisco, CA 94104; phone: 415-433-1740; fax: 800-605-2665; World Wide Web: www.josseybass.com ($21.95). Retrieved fromeric.ed.gov/?id=ed415771
    [Google Scholar]
  31. Miller, S
    (2014) Collaborative Approaches Needed to Close the Big Data Skills Gap. Journal of Organization Design, 3(1), 26–30. doi: 10.7146/jod.3.1.9823
    https://doi.org/10.7146/jod.3.1.9823 [Google Scholar]
  32. Neuhauser, A
    (2015, June29). 2015 STEM Index Shows Gender, Racial Gaps Widen. RetrievedSeptember 12, 2016, fromwww.usnews.com/news/stem-index/articles/2015/06/29/gender-racial-gaps-widen-in-stem-fields
  33. O’Neil, Cathy
    2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
    [Google Scholar]
  34. Pasquale, Frank
    2015The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge: Harvard University Press. doi: 10.4159/harvard.9780674736061
    https://doi.org/10.4159/harvard.9780674736061 [Google Scholar]
  35. Phillips, A
    (1991) Engendering democracy. University Park, Pa.: Pennsylvania State University Press.
    [Google Scholar]
  36. Sandvig, C. , Hamilton, K. , Karahalios, K. & Langbort, C
    (2014) Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry. Retrieved fromhttps://pdfs.semanticscholar.org/b722/7cbd34766655dea10d0437ab10df3a127396.pdf
    [Google Scholar]
  37. Tufekci, Z
    (2014a) Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). Retrieved fromfirstmonday.org/ojs/index.php/fm/article/view/4901 doi: 10.5210/fm.v19i7.4901
    https://doi.org/10.5210/fm.v19i7.4901 [Google Scholar]
  38. (2014b) Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). Retrieved fromfirstmonday.org/ojs/index.php/fm/article/view/4901 doi: 10.5210/fm.v19i7.4901
    https://doi.org/10.5210/fm.v19i7.4901 [Google Scholar]
  39. Tygel, A. & Kirsch, R
  40. Walters, S. & Manicom, L
    (1996) Gender in Popular Education. Methods for Empowerment. ERIC. Retrieved fromeric.ed.gov/?id=ED398449
    [Google Scholar]
  41. Welles, B.F
    (2014) On minorities and outliers: The case for making Big Data small. Big Data & Society, 1(1), 2053951714540613. doi: 10.1177/2053951714540613
    https://doi.org/10.1177/2053951714540613 [Google Scholar]
  42. Wickham, H
    (2014) Tidy Data. Journal of Statistical Software, 59(10). doi: 10.18637/jss.v059.i10
    https://doi.org/10.18637/jss.v059.i10 [Google Scholar]
  43. Williams, S. , Deahl, E. , Rubel, L. & Lim, V
    (2014) City Digits: Local Lotto: Developing Youth Data Literacy by Investigating the Lottery. Journal of Digital and Media Literacy, 2(2). Retrieved fromwww.jodml.org/2014/12/15/city-digits-local-lotto-developing-youth-data-literacy-by-investigating-the-lottery/
    [Google Scholar]
  44. Yin, R.K
    (2013) Case study research: Design and methods. Sage publications. Retrieved fromscholar.google.com/scholar?cluster=17071144043372907427&hl=en&oi=scholarr
    [Google Scholar]
  45. Zimmerman, B.J
    (2000) Self-Efficacy: An Essential Motive to Learn. Contemporary Educational Psychology, 25(1), 82–91. doi: 10.1006/ceps.1999.1016
    https://doi.org/10.1006/ceps.1999.1016 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/idj.23.1.03dig
Loading
  • Article Type: Research Article
Keyword(s): data literacy , data visualization , empowerment and inequality
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error