Using gamification to increase map data production during humanitarian volunteered geographic information (VGI) campaigns
Bibliographic record
Abstract
Volunteered geographic information (VGI) offers a solution to inequalities in authoritative map data that can limit our response to humanitarian crises. However, sustaining voluntary contributions of map data can be difficult and hybrid machine learning-VGI (ML-VGI) workflows developed to encourage sustained volunteer contributions have been demonstrated to be insufficient. Gamification can be used to encourage volunteers to map for longer, however evaluations of gamification to increase humanitarian mapping contributions are rare. Here we develop a gamified humanitarian ML-VGI mapping platform (“Map Safari”) and evaluate the use of game elements to encourage sustained volunteer contributions without reducing contribution quality. Our results suggest that gamification makes mapping more fun, particularly for first time mappers, without degrading map data quality. Competition is demonstrated to be important for encouraging enjoyment of game elements and increasing map data contributions. Future gamified mapping platforms should emphasize competition and ensure there are enough game elements to make platform use feel game-like. This research demonstrates that gamification can be used to encourage continued voluntary contributions of map data thereby increasing the amount of map data available to humanitarian organizations.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".