Abstract
We apply Lave & Wenger's construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention - as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.
- Emily M. Bender and Batya Friedman. 2018. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics 6 (12 2018), 587--604. https://doi.org/10.1162/tacl_a_00041Google Scholar
- Kirsten Boehner and Carl DiSalvo. 2016. Data, Design and Civics: An Exploratory Study of Civic Tech. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI '16). Association for Computing Machinery, New York, NY, USA, 2970--2981. https://doi.org/10.1145/2858036.2858326Google ScholarDigital Library
- Chris Bopp, Ellie Harmon, and Amy Voida. 2017. Disempowered by Data: Nonprofits, Social Enterprises, and the Consequences of Data-Driven Work. Association for Computing Machinery, New York, NY, USA, 3608--3619. https://doi.org/10.1145/3025453.3025694Google ScholarDigital Library
- Geoffrey C. Bowker. 2005. Memory practices in the sciences. MIT Press, Cambridge, Mass. OCLC: ocm60776866.Google Scholar
- Virginia Braun, Victoria Clarke, Nikki Hayfield, and Gareth Terry. 2019. Thematic Analysis. In Handbook of Research Methods in Health Social Sciences, Pranee Liamputtong (Ed.). Springer Singapore, Singapore, 843--860. https://doi.org/10.1007/978--981--10--5251--4_103Google Scholar
- Susan L. Bryant, Andrea Forte, and Amy Bruckman. 2005. Becoming Wikipedian: Transformation of Participation in a Collaborative Online Encyclopedia. In Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work (Sanibel Island, Florida, USA) (GROUP '05). Association for Computing Machinery, New York, NY, USA, 1--10. https://doi.org/10.1145/1099203.1099205Google ScholarDigital Library
- Longbing Cao. 2017. Data Science: A Comprehensive Overview. Comput. Surveys 50, 3 (Oct. 2017), 1--42. https://doi.org/10.1145/3076253Google ScholarDigital Library
- Silvia Cazacu, Nicolai Brodersen Hansen, and Ben Schouten. 2020. Empowerment Approaches in Digital Civics. In 32nd Australian Conference on Human-Computer Interaction (Sydney, NSW, Australia) (OzCHI '20). Association for Computing Machinery, New York, NY, USA, 692--699. https://doi.org/10.1145/3441000.3441069Google ScholarDigital Library
- Anamaria Crisan, Brittany Fiore-Gartland, and Melanie Tory. 2021. Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2 2021), 1860--1870. https://doi.org/10.1109/TVCG.2020.3030340Google ScholarCross Ref
- Dharma Dailey and Kate Starbird. 2017. Social Media Seamsters: Stitching Platforms & Audiences into Local Crisis Infrastructure. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). Association for Computing Machinery, New York, NY, USA, 1277--1289. https://doi.org/10.1145/2998181.2998290Google ScholarDigital Library
- Sayamindu Dasgupta and Benjamin Mako Hill. 2017. Scratch Community Blocks: Supporting Children as Data Scientists. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3620--3631. https://doi.org/10.1145/3025453.3025847 [Online; accessed 2020--10--18].Google ScholarDigital Library
- Yuri Demchenko, Luca Comminiello, and Gianluca Reali. 2019. Designing Customisable Data Science Curriculum Using Ontology for Data Science Competences and Body of Knowledge. In Proceedings of the 2019 International Conference on Big Data and Education - ICBDE'19. ACM Press, London, United Kingdom, 124--128. https://doi.org/10.1145/3322134.3322143Google ScholarDigital Library
- Catherine D'Ignazio and Lauren F. Klein. 2020. Data feminism. The MIT Press, Cambridge, Massachusetts.Google Scholar
- Jaimie Drozdal, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, and Hui Su. 2020. Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems. In Proceedings of the 25th International Conference on Intelligent User Interfaces. ACM, Cagliari Italy, 297--307. https://doi.org/10.1145/3377325.3377501Google ScholarDigital Library
- Rosalind Edwards and Janet Holland. 2013. What is qualitative interviewing? Bloomsbury, London : New Delhi. OCLC: ocn855705441.Google Scholar
- Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding Explainability: Towards Social Transparency in AI systems. arXiv:2101.04719 [cs] (Jan. 2021). https://doi.org/10.1145/3411764.3445188 arXiv:2101.04719.Google ScholarDigital Library
- Sheena Erete and Jennifer O. Burrell. 2017. Empowered Participation: How Citizens Use Technology in Local Governance. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 2307--2319. https://doi.org/10.1145/3025453.3025996Google ScholarDigital Library
- Melanie Feinberg. 2017. A Design Perspective on Data. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2952--2963. https://doi.org/10.1145/3025453.3025837 [Online; accessed 2020-08--17].Google ScholarDigital Library
- Melanie Feinberg. 2017. Reading databases: slow information interactions beyond the retrieval paradigm. Journal of Documentation 73, 2 (March 2017), 336--356. https://doi.org/10.1108/JD-03--2016-0030Google ScholarCross Ref
- Melanie Feinberg. 2017. The value of discernment: making use of interpretive flexibility in metadata generation and aggregation. Information Research-an International Electronic Journal 22, 1 (2017), 22.Google Scholar
- Melanie Feinberg, Daniel Carter, Julia Bullard, and Ayse Gursoy. 2017. Translating Texture: Design as Integration. In Proceedings of the 2017 Conference on Designing Interactive Systems. ACM, Edinburgh United Kingdom, 297--307. https://doi.org/10.1145/3064663.3064730Google ScholarDigital Library
- Melanie Feinberg, Will Sutherland, Sarah Beth Nelson, Mohammad Hossein Jarrahi, and Arcot Rajasekar. 2020. The New Reality of Reproducibility: The Role of Data Work in Scientific Research. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (May 2020), 1--22. https://doi.org/10.1145/3392840Google ScholarDigital Library
- Uwe Flick. 2009. An introduction to qualitative research. https://nls.ldls.org.uk/welcome.html?ark:/81055/vdc_100025409254.0x000001 OCLC: 1052103480.Google Scholar
- Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2020. Datasheets for Datasets. arXiv:1803.09010 [cs] (19 3 2020). http://arxiv.org/abs/1803.09010 arXiv:1803.09010.Google Scholar
- Lisa Gitelman (Ed.). 2013. "Raw data" is an oxymoron. The MIT Press, Cambridge, Massachusetts ; London, England.Google Scholar
- Lisa M. Given (Ed.). 2008. The Sage encyclopedia of qualitative research methods. Sage Publications, Los Angeles, Calif.Google Scholar
- Lisa Hardy, Colin Dixon, and Sherry Hsi. 2020. From Data Collectors to Data Producers: Shifting Students' Relationship to Data. Journal of the Learning Sciences 29, 1 (Jan. 2020), 104--126. https://doi.org/10.1080/10508406.2019.1678164Google ScholarCross Ref
- Samantha Hautea, Sayamindu Dasgupta, and Benjamin Mako Hill. 2017. Youth Perspectives on Critical Data Literacies. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 919--930. https://doi.org/10.1145/3025453.3025823 [Online; accessed 2020-09--15].Google ScholarDigital Library
- Sarah Inman and David Ribes. 2019. "Beautiful Seams": Strategic Revelations and Concealments. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI '19). Association for Computing Machinery, New York, NY, USA, 1--14. https://doi.org/10.1145/3290605.3300508Google ScholarDigital Library
- Lilly Irani and Jesse Marx. 2021. Redacted. Taller California, San Diego, CA.Google Scholar
- Abhinav Jain, Hima Patel, Lokesh Nagalapatti, Nitin Gupta, Sameep Mehta, Shanmukha Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, and Vitobha Munigala. 2020. Overview and Importance of Data Quality for Machine Learning Tasks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3561--3562. https://doi.org/10.1145/3394486.3406477 [Online; accessed 2021-02--21].Google ScholarDigital Library
- Britney Johnson, Ben Rydal Shapiro, Betsy DiSalvo, Annabel Rothschild, and Carl DiSalvo. 2021. Exploring Approaches to Data Literacy Through a Critical Race Theory Perspective. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445141Google ScholarDigital Library
- Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: interactive visual specification of data transformation scripts. In Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11. ACM Press, Vancouver, BC, Canada, 3363. https://doi.org/10.1145/1978942.1979444Google ScholarDigital Library
- Charles Kiene, Kenny Shores, Eshwar Chandrasekharan, Shagun Jhaver, Jialun "Aaron" Jiang, Brianna Dym, Joseph Seering, Sarah Gilbert, Kat Lo, Donghee Yvette Wohn, and Bryan Dosono. 2019. Volunteer Work: Mapping the Future of Moderation Research. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing (Austin, TX, USA) (CSCW '19). Association for Computing Machinery, New York, NY, USA, 492--497. https://doi.org/10.1145/3311957.3359443Google ScholarDigital Library
- Antti Knutas, Victoria Palacin, Giovanni Maccani, and Markus Helfert. 2019. Software Engineering in Civic Tech a Case Study about Code for Ireland. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Society (Montreal, Quebec, Canada) (ICSE-SEIS '19). IEEE Press, 41--50. https://doi.org/10.1109/ICSE-SEIS.2019.14Google ScholarDigital Library
- Laura Koesten, Kathleen Gregory, Paul Groth, and Elena Simperl. 2021. Talking datasets -- Understanding data sensemaking behaviours. International Journal of Human-Computer Studies 146 (2 2021), 102562. https://doi.org/10.1016/j.ijhcs.2020.102562Google ScholarCross Ref
- Marina Kogan, Aaron Halfaker, Shion Guha, Cecilia Aragon, Michael Muller, and Stuart Geiger. 2020. Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work. ACM, Sanibel Island Florida USA, 151--156. https://doi.org/10.1145/3323994.3369898Google ScholarDigital Library
- Jean Lave and Etienne Wenger. 1991. Situated learning: legitimate peripheral participation. Cambridge University Press, Cambridge [England] ; New York.Google Scholar
- Yanni A. Loukissas. 2019. All data are local: thinking critically in a data-driven society. The MIT Press, Cambridge, Massachusetts.Google Scholar
- Luis Felipe Luna-Reyes. 2018. The search for the data scientist: creating value from data. ACM SIGCAS Computers and Society 47, 4 (July 2018), 12--16. https://doi.org/10.1145/3243141.3243145Google ScholarDigital Library
- Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, and Aleksandra Mojsilovic. 2019. How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question? Proceedings of the ACM on Human-Computer Interaction 3, GROUP (5 12 2019), 1--23. https://doi.org/10.1145/3361118Google ScholarDigital Library
- Gary Marchionini. 2017. Information Science Roles in the Emerging Field of Data Science. Journal of Data and Information Science 1, 2 (Sept. 2017), 1--6. https://doi.org/10.20309/jdis.201609Google Scholar
- Amanda Meng. 2014. Investigating the Roots of Open Data's Social Impact. JeDEM - eJournal of eDemocracy and Open Government 6, 1 (Oct. 2014), 1--13. https://doi.org/10.29379/jedem.v6i1.288Google ScholarCross Ref
- Amanda Meng, Carl DiSalvo, Lokman Tsui, and Michael Best. 2019. The social impact of open government data in Hong Kong: Umbrella Movement protests and adversarial politics. The Information Society 35, 4 (Aug. 2019), 216--228. https://doi.org/10.1080/01972243.2019.1613464Google ScholarCross Ref
- Amanda Meng, Carl DiSalvo, and Ellen Zegura. 2019. Collaborative Data Work Towards a Caring Democracy. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 42 (Nov. 2019), 23 pages. https://doi.org/10.1145/3359144Google ScholarDigital Library
- Milagros Miceli, Martin Schuessler, and Tianling Yang. 2020. Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (14 10 2020), 1--25. https://doi.org/10.1145/3415186Google ScholarDigital Library
- Milagros Miceli, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Serbanescu, and Alex Hanna. 2021. Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 161--172. https://doi.org/10.1145/3442188.3445880 [Online; accessed 2021-03--10].Google ScholarDigital Library
- Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. 2016. The ethics of algorithms: Mapping the debate. Big Data & Society 3, 2 (Dec. 2016), 205395171667967. https://doi.org/10.1177/2053951716679679Google ScholarCross Ref
- Thema Monroe-White. 2021. Emancipatory Data Science: A Liberatory Framework for Mitigating Data Harms and Fostering Social Transformation. In Proceedings of the 2021 on Computers and People Research Conference (Virtual Event, Germany) (SIGMIS-CPR'21). Association for Computing Machinery, New York, NY, USA, 23--30. https://doi.org/10.1145/3458026.3462161Google ScholarDigital Library
- Michael Muller, Cecilia Aragon, Shion Guha, Marina Kogan, Gina Neff, Cathrine Seidelin, Katie Shilton, and Anissa Tanweer. 2020. Interrogating Data Science. Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing, 467--473. https://doi.org/10.1145/3406865.3418584 [Online; accessed 2021-02--21].Google ScholarDigital Library
- Michael Muller and Thomas Erickson. 2018. In the Data Kitchen: A Review (a design fiction on data science). In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, Montreal QC Canada, 1--10. https://doi.org/10.1145/3170427.3188407Google ScholarDigital Library
- Michael Muller, Melanie Feinberg, Timothy George, Steven J. Jackson, Bonnie E. John, Mary Beth Kery, and Samir Passi. 2019. Human-Centered Study of Data Science Work Practices. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA '19). Association for Computing Machinery, New York, NY, USA, 1--8. https://doi.org/10.1145/3290607.3299018Google ScholarDigital Library
- Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1--15. https://doi.org/10.1145/3290605.3300356Google ScholarDigital Library
- Gina Neff, Anissa Tanweer, Brittany Fiore-Gartland, and Laura Osburn. 2017. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data 5, 2 (June 2017), 85--97. https://doi.org/10.1089/big.2016.0050Google ScholarCross Ref
- US Department of Labor Statistics. 2021. Labor Force Statistics from the Current Population Survey. https://www.bls.gov/cps/cpsaat11.htm [Online; accessed 2021-03--30].Google Scholar
- Samir Passi and Steven Jackson. 2017. Data Vision: Learning to See Through Algorithmic Abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, Portland Oregon USA, 2436--2447. https://doi.org/10.1145/2998181.2998331Google ScholarDigital Library
- Paula Pereira, Jacome Cunha, and Joao Paulo Fernandes. 2020. On Understanding Data Scientists. 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 1--5. https://doi.org/10.1109/VL/HCC50065.2020.9127269 [Online; accessed 2021-02--21].Google Scholar
- Nicole Perlroth. 2021. This is how they tell me the world ends: the cyberweapons arms race. Bloomsbury Publishing, New York.Google Scholar
- Kathleen H. Pine, Claus Bossen, Yunan Chen, Gunnar Ellingsen, Miria Grisot, Melissa Mazmanian, and Naja Holten Møller. 2018. Data Work in Healthcare: Challenges for Patients, Clinicians and Administrators. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (Jersey City, NJ, USA) (CSCW '18). Association for Computing Machinery, New York, NY, USA, 433--439. https://doi.org/10.1145/3272973.3273017Google ScholarDigital Library
- David Ribes. 2017. Notes on the Concept of Data Interoperability: Cases from an Ecology of AIDS Research Infrastructures. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, Portland Oregon USA, 1514--1526. https://doi.org/10.1145/2998181.2998344Google ScholarDigital Library
- Sarah T. Roberts. [n.d.]. Behind the screen: content moderation in the shadows of social media. Yale University Press. OCLC: on1055263168.Google Scholar
- Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora Aroyo. 2021. "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2021), 15.Google ScholarDigital Library
- Morgan Klaus Scheuerman, Emily Denton, and Alex Hanna. 2021. Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development. CoRR abs/2108.04308 (2021). arXiv:2108.04308 https://arxiv.org/abs/2108.04308Google Scholar
- Caroline Sinders. 2020. A Solution without a Problem? Seeking Questions to Ask and Problems to Solve within Open, Civic Data. Interactions 27, 5 (Sept. 2020), 46--49. https://doi.org/10.1145/3411292Google ScholarDigital Library
- Barry Smart, Kay Peggs, and Joseph D. Burridge (Eds.). 2013. Observation methods. SAGE, Los Angeles. OCLC: ocn816163690.Google Scholar
- Olivier St-Cyr, Craig M. MacDonald, Elizabeth F. Churchill, Jenny J. Preece, and Anna Bowser. 2018. Developing a Community of Practice to Support Global HCI Education. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI EA '18). Association for Computing Machinery, New York, NY, USA, 1--7. https://doi.org/10.1145/3170427.3170616Google ScholarDigital Library
- Anselm L. Strauss and Juliet M. Corbin (Eds.). 1997. Grounded theory in practice. Sage Publications, Thousand Oaks.Google Scholar
- Janet Vertesi. 2014. Seamful Spaces: Heterogeneous Infrastructures in Interaction. Science, Technology, & Human Values 39, 2 (2014), 264--284. https://doi.org/10.1177/0162243913516012 arXiv:https://doi.org/10.1177/0162243913516012Google Scholar
- April Yi Wang, Anant Mittal, Christopher Brooks, and Steve Oney. 2019. How Data Scientists Use Computational Notebooks for Real-Time Collaboration. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (Nov. 2019), 1--30. https://doi.org/10.1145/3359141Google ScholarDigital Library
- Dakuo Wang, Josh Andres, Justin Weisz, Erick Oduor, and Casey Dugan. 2021. AutoDS: Towards Human-Centered Automation of Data Science. arXiv:2101.05273 [cs] (13 1 2021). https://doi.org/10.1145/3411764.3445526 arXiv:2101.05273.Google ScholarDigital Library
- Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019. Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (Nov. 2019), 1--24. https://doi.org/10.1145/3359313Google ScholarDigital Library
- Michelle Hoda Wilkerson, Kathryn Lanouette, Rebecca L Shareff, Tim Erickson, Nicole Bulalacao, Joan I Heller, Natalya St Clair, William Finzer, and Frieda Reichsman. 2018. Data Transformations: Restructuring Data for Inquiry in a Simulation and Data Analysis Environment. International Conference for the Learning Sciences (2018), 2.Google Scholar
- Amy X. Zhang, Michael Muller, and Dakuo Wang. 2020. How do Data Science Workers Collaborate? Roles, Workflows, and Tools. arXiv:2001.06684 [cs, stat] (16 4 2020). http://arxiv.org/abs/2001.06684 arXiv: 2001.06684.Google Scholar
Index Terms
- Interrogating Data Work as a Community of Practice
Recommendations
Remote, but Connected: How #TidyTuesday Provides an Online Community of Practice for Data Scientists.
CSCWData science practitioners face the challenge of continually honing their skills such as data wrangling and visualization. As data scientists seek online spaces to network, learn and share resources with one another, each individual has to employ their ...
Support Newcomer's Learning in Community of Practice: In Terms of Legitimate Peripheral Participation
ICEE '10: Proceedings of the 2010 International Conference on E-Business and E-GovernmentCommunity of practice is critical to organization learning. This paper takes the newcomer as subject. It analyzes the factors affected newcomer’s legitimate participation and peripheral participation, then it suggests how to support newcomer’s learning ...
Community Design: growing one's own information infrastructure
PDC '08: Proceedings of the Tenth Anniversary Conference on Participatory Design 2008This paper examines the phenomenon of Community Design. It is a radical phenomenon in that community members collectively grow their own community information infrastructures without the intervention of professionals typically associated with such ...
Comments