Mobile Gamification for Crowdsourcing Data Collection
Bibliographic record
Abstract
Classic ways of gathering data on human behaviour are time-consuming, costly and are subject to limited participant pools. Crowdsourcing offers a reduction in operating costs and access to a diverse and large participant pool; however issues arise concerning low worker pay and questions about data quality. Gamification provides a motivation to participate, but also requires the development of specialized, research-question specific games that can be costly to produce. Our solution combines gamification and crowdsourcing in a smartphone-based system that emulates the popular Freemium model of play to motivate voluntary participation through in-game rewards, using a robust framework to study multiple unrelated research questions within the same system. We deployed our game on the Android store and compared it to a gamified laboratory version and a non-gamified laboratory version, and found that players who used the in-game rewards were motivated to do experimental tasks. There was no difference between the systems for performance on a motor task; however, performance on the cognitive task was worse for the crowdsourced game. We discuss options for improving performance on tasks requiring attention.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".