We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers

By N. Salehi, Lilly C. Irani, Michael S. Bernstein, Alia Al-Khatib, A. Alkhatib, Eva Ogbe, Kristy Milland, Clickhappier. 2015

329 citations recorded by the source corpus.

Subfields: Computer Science Applications, Human-Computer Interaction, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Ranjay Krishna
2. Michael S. Bernstein
3. Adam Marcus
4. Aditya G. Parameswaran
5. Li Fei-Fei
6. Kenji Hata
7. Jeffrey P. Bigham
8. Grant Hugh

In Human-Computer Interaction

1. Michael S. Bernstein
2. Jeffrey P. Bigham
3. Grant Hugh
4. Mark E. Whiting
5. Kotaro Hara
6. Susumu Saito
7. Toni Kaplan
8. Ethan Fast

In Social Sciences & Humanities

1. Michael S. Bernstein
2. Dubravka Cecez-Kecmanovic
3. Ellie Harmon
4. Daniel Schlagwein
5. Ethan Fast
6. Tina Vachovsky
7. Li Fei-Fei
8. Benjamin Hanckel

Citations in the corpus

• Community building on crowdwork platforms: Autonomy and control of online workers? (2020, background): “Some authors point to crowdworkers’ attempts for selforganization through external forums (e.g. Turkernation, Dynamo) or engage in actionoriented research to identify how common solidarities and collective action can be promoted (Graham and Woodcock, 2018; Salehi et al., 2015).”
• Embracing Error to Enable Rapid Crowdsourcing (2016, background): “17 to label a stream of 100 images (resulting in a wage of $6 per hour [51]).”
• Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2022, unknown): “For comparison, we also ask AMT workers to rate the plausibility of 100 generated articles from G ROVER - GEN .”
• Striving to Earn More: A Survey of Work Strategies and Tool Use Among Crowd Workers (2018, background): “The AMT marketplace demonstrates a severely uneven distribution of power and information between workers and requesters (Salehi et al. 2015; Irani and Silberman 2013 (Gaikwad et al. 2015).”
• Empath: Understanding Topic Signals in Large-Scale Text (2016, methodology): “We ask three independent workers to complete each task at a cost of $0.14 per task, resulting in an hourly wage in line with Ethical Guidelines for AMT research [35].”
• Ethical norms and issues in crowdsourcing practices: A Habermasian analysis (2018, methodology): “Notably, these include studies based on a view of (microwork) crowdsourcing as ethically problematic (Irani and Silberman 2014; Silberman et al. 2010), with Irani and colleagues providing tools such as Turkopticon (Irani and Silberman 2013) and Dynamo (Salehi et al. 2015) to support crowd workers.”
• Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “We first note that the study of crowd workers is relatively well-explored, with several seminal and ongoing surveys of different crowds over time [161, 99, 177, 165].”
• Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community (2016, methodology): “Each task gathers annotations for 20 words, for which we pay workers $0.14 in line with guidelines for ethical research (Salehi, Irani, and Bernstein 2015).”
• Digital labour platforms and the future of work (2018, background): “Importantly, because there were different worker sub-communities with varying opinions, the researchers created an independent discussion forum where experienced workers could deliberate anonymously, without inter-community “drama” (Salehi et al., 2015).”
• Daemo: A Self-Governed Crowdsourcing Marketplace (2015, background): “Several venues such as Turker Nation and Dynamo Forums have been created so that workers’ opinion can be heard [3, 4].”