The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
CrowdForge: crowdsourcing complex work
Abstract
Micro-task markets such as Amazon's Mechanical Turk represent a new paradigm for accomplishing work, in which employers can tap into a large population of workers around the globe to accomplish tasks in a fraction of the time and money of more traditional methods. However, such markets have been primarily used for simple, independent tasks, such as labeling an image or judging the relevance of a search result. Here we present a general purpose framework for accomplishing complex and interdependent tasks using micro-task markets. We describe our framework, a web-based prototype, and case studies on article writing, decision making, and science journalism that demonstrate the benefits and limitations of the approach.
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Cites
The interdisciplinary study of coordination
Malone, T. W., & Crowston, K. (1994) · ACM Computing Surveys · doi:10.1145/174666.174668 · Cited by 3,504
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,231 · cited 3×
“A large number of studies have examined the usefulness of MTurk as a platform for collecting and evaluating data for applications including machine translation [4] image databases [7], and natural language processing [22].”
BackgroundMethodDeterminants of Coordination Modes within Organizations
Ven, A. H. V. D., Delbecq, A. L., & Koenig, R. (1976) · American Sociological Review · doi:10.2307/2094477 · Cited by 1,979
Cited by
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441923 · Cited by 1,034
Self-citationLearning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
PromptChainer: Chaining Large Language Model Prompts through Visual Programming
Wu, T., Jiang, E., Donsbach, A., Gray, J., Molina, A., Terry, M., & Cai, C. J. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519729 · Cited by 198
Examining Crowd Work and Gig Work Through The Historical Lens of Piecework
Alkhatib, A., Bernstein, M. S., & Levi, M. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025974 · Cited by 148
Metadata
- Authors
- Kittur, A., Smus, B., Khamkar, S., & Kraut, R. E.
- Venue
- Proceedings of the 24th annual ACM symposium on User interface software and technology
- Publisher
- ACM
- Published
- 2011-10-16
- Pages
- 43-52
- OpenAlex
- W4230939871
- Cited by
- 309
- References
- 3
- FWCI
- 15.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
