Article proposes a critique of a policy or practice with specific action proposals or suggestions.
Article follows conventions of academic research article e.g. position in literature, cited sources, and claimed contribution.
Article is based on developments that have not yet occurred.
Article is based on formal logic or mathematical technique.
Language quality: 3/3*
Standard of English expression in article is excellent.
Scope of debate: 0/3
Article addresses an issue which is widely known and debated.
Most related sources are mentioned in article [this is an invitation to careful selection rather than a demonstration of prowess in citation collection i.e. apt and representative choices made in source citations].
Logical flow: 3/3*
Ideas are well organised in article.
The argument presented in article is new.
Review impact: 1/3
The article has been significantly changed as a result of the review process.
Reviewers indicate their appreciation of the article in the form of a 50 word statement.
Approaching crowdsourced science through the lens of labor provides a refreshing take on these processes. This may be more useful to a critical understanding of contemporary citizen science than the democratization rhetorics that still inform much STS scholarship on this topic.
The paper combines two areas of literature and reflexive work that were previously dissociated: citizen science studies and labour, specifically crowdsourced labour studies. By looking at the case of Zooniverse – a crowdsourced citizen science platform – the paper provides a significant case study demonstrating the questionable labour practices possible under the participatory promise of citizen science.
This article is based on an interesting fieldwork of citizen science and highlights the contradiction in crowdsourcing platforms. Efficiency and rigorous work in scientific work are counterbalanced by isolated micro tasks without sense for a part of volunteers. That result highlights the importance of explanation from scientists concerning their approach to large datasets.