Storm Crowds: Evidence from Zooniverse on Crowd Contribution Design
Bibliographic record
Abstract
Crowdsourcing - a collaborative form of content production based on the contributions of large groups of individuals - has proliferated in the past decade. Due to this growth, recent research has focused on understanding the factors that affect its sustainability. Prior studies have highlighted the importance of volunteers’ prosocial motivations, the sense of belong- ing to a community, and symbolic rewards within crowdsourcing websites. One factor that has received limited attention in the existing literature is how the design of crowdsourcing platforms affects their sustainability. We study whether the design element - particularly, the divisibility of contributions (i.e. whether contributing tasks are bundled together or can be carried out separately) - is a factor that affects the level and quality of crowdsourcing contributions. We investigate this in the context of Zooniverse, the world’s largest crowd- sourced science site, in which volunteers contribute to scientific research by performing data processing tasks. Our choice of empirical setting is motivated by the fact that one of the Zooniverse projects, Cyclone Center, underwent a format change that decreased the divisibil- ity of contributions, by bundling together two tasks that were previously separate. We refer to contributions for which both tasks were done as complete, and contributions for which only one task was done as incomplete. In this context, we develop a theoretical model that predicts (i) a positive relationship between contribution divisibility and the total number of contributions (i.e. complete and incomplete) per volunteer, as well as (ii) an ambiguous relationship between contribution divisibility and the number of complete contributions per volunteer. We test these predictions empirically by exploiting the format change in Cyclone Center. We find that after the format change, which decreased contribution divisibility, (i) the total number of contributions per volunteer decreased, and (ii) the number of complete contributions made by anonymous volunteers increased, while that made by registered volunteers remained unchanged. We also find that these results have positive implications for the quality of complete contributions. Our results have strategic implications for crowdsourcing platforms because they suggest that the design of crowdsourcing platforms, specifically the divisibility of contributions, is a factor that matters for their sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".