Improving digital citizen science by learning from volunteer practices in biodiversity monitoring
Bibliographic record
Abstract
<b>This summary report offers citizen science practitioners research insights and recommendations for community engagement in support of the ongoing transition to digital citizen science.</b>Over 50% of biodiversity records submitted to Australia’s national biodiversity database (the Atlas of Living Australia) are contributed by volunteer citizen scientists. Most of these records are generated through “contributory” citizen science programs, in which research design, data analysis, dissemination, and other practices are the responsibility of the organizing institutions, while volunteers are invited to primarily contribute data. These citizen science programs are increasingly using digital tools - such as smartphone apps and web portals - to facilitate data collection by volunteers.It is critical to the success of these programs to understand the experiences of citizen scientists in engaging with biodiversity and contributing to monitoring through such digital tools. This can help inform program design to improve participation and increase data quantity and quality, supporting the conservation of Australia’s biodiversity.This social science research project asked:What are the knowledge practices of biodiversity citizen scientists (do they ‘just’ collect data in contributory citizen science)?In what ways are digital technologies re-shaping the participation of volunteers?How do volunteers care about and for the digital data they produce?The research investigated two contributory biodiversity citizen science programs in Australia: one in which volunteers are invited to monitor frog populations and the other, the breeding success of birds. Both programs have run for over 20 years and have shifted from analogue technologies (such as cassette recordings and pen-and-paper surveys) to digital surveys for data collection (smartphone app and website portal). Nowadays, both programs also offer digital training to volunteers.<b>Cite as:</b> Gonzalez Canada, D., Lavau, S. and Williams, K.J.H. (2024) <i>Improving digital citizen science by learning from volunteer practices in biodiversity monitoring </i>(Technical Report No. 24.8). Melbourne Waterway Research-Practice Partnership. http://doi.org/10.26188/28636739
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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 | 0.000 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".