Designing Context-Aware Urban Citizen Science Systems for Sustained Citizen Engagement: A Pilot Study in Urban Heat Island Detection
Bibliographic record
Abstract
This short paper explores the design of context-aware urban citizen science systems to address the challenge of sustained participant engagement. Building on a pilot study conducted in Zurich involving citizens in urban heat island detection, we investigate engagement barriers and patterns. Grounded in Information Systems (IS) Continuance Theory and leveraging Just-In-Time Adaptive Interventions (JITAIs), we follow a design science research approach and propose initial design requirements and principles for enhancing citizen science systems. These principles include context-aware participation timing, real-time feedback, and adaptive task complexity, aimed at fostering satisfaction, perceived usefulness, and expectation confirmation. Our initial pilot study insights and theoretical analysis indicate that contextual factors may play an important role in moderating user engagement and system interactions. We conclude with insights on system design that align with theoretical models of IS continuance, offering guidance for practical applications and future research in developing context-aware citizen science platforms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".