Mobile Gamification for Crowdsourcing Data Collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it