Citizen scientists for <scp>MoveApps</scp> : Innovations and insights from volunteer coders in wildlife conservation
Bibliographic record
Abstract
Abstract Amidst numerous global crises, decision‐makers have recognized the critical need for fact‐based advice, driving unprecedented data collection. However, a significant gap persists between data availability and knowledge generation, primarily due to time and resource constraints. To bridge this gap, we propose involving a novel group of citizen scientists: volunteer code developers. Utilizing the modular, open‐source analysis platform MoveApps, we were able to engage 12 volunteer coders in a challenge to create tools for movement ecology, aimed at animal conservation. These volunteers developed functioning applications capable of analysing animal tracking data to identify stationary behaviour, estimate ranges and movement corridors and assess human–wildlife conflicts using data sets from human infrastructure, such as OpenStreetMap. Engaging citizen scientists in developing code has surfaced three primary challenges: (i) Community Building—attracting the right participants; (ii) Community Involvement—maintaining quality standards and directing tasks effectively; and (iii) Community Retention—ensuring long‐term engagement. We explore strategies to overcome these challenges and share lessons learnt from our coding challenge experience. Our approaches include engaging the community through their own preferred channels, providing an accessible open‐source tool, defining specific use cases in detail, ensuring quality through feedback, fostering self‐organized community exchanges and prominently illustrating the impact of contributions. We also advocate for other disciplines to consider leveraging volunteer involvement, alongside artificial intelligence, for data analysis and generating state‐of‐the‐art, fact‐based insight to address critical issues such as the global decline in biodiversity.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".