Using gamification elements for competitive crowdsourcing: exploring the underlying mechanism
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
Gamification can be an effective mechanism of engaging individual users (i.e. solvers) in task solving on competitive crowdsourcing platforms. However, past literature lacks a nuanced understanding of how gamification elements can affect solvers’ crowdsourcing behaviour via intrinsic and extrinsic motivations. We conceptualised two typical gamification elements (points and immediate performance feedback). Borrowing from self-determination theory, we modelled the effects of points and immediate performance feedback on both intrinsic and extrinsic motivations that, in turn, affect solvers’ crowdsourcing participation. Using a survey data of 295 solvers from a large competitive crowdsourcing platform in China, we found that points are positively related to intrinsic and extrinsic motivations, while immediate performance feedback only enhances intrinsic motivation. Both intrinsic and extrinsic motivations positively affect solvers’ crowdsourcing participation. The findings of this study enrich our understanding of the mechanisms of the two gamification elements and provide practical insights on how to enhance solvers’ participation in crowdsourcing.
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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 | 0.000 | 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 | 0.000 | 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