Can Citizen Science learn something from Pokemon-Go?
Bibliographic record
Abstract
Citizen science is a symbiosis between scientists and the wider public. Conventional science is based on systematic, repeated observations, and methodically testing theories in controlled experiments. NatureWatch NZ, the largest citizen bioscience platform in the country, has in 4 years attracted 0.001% of NZ’s population to record over a quarter million observations of everything from kittens, first encounters, spread of aliens, biodiversity hotspots, to forest industry compilations of biodiversity. NatureWatch NZ is running at 4-8 times the level of uptake in the American parent iNaturalist. In a parallel universe, Pokemon-Go (PG) has taken the world by storm, drawing people out of their homes, into streets and parks, in unprecedented numbers to hunt down Pokemon! We explore how digital culture advances nature conservation goals and whether gaming successfully converts that desire for discovery and primal need for adrenaline into robust data. It’s been observed that if PG fanatics were making natural history observations we would accumulate the equivalent of centuries of field data in the space of a few days! The long game is to get meaningful, enduring engagement that demonstrates market share to funders and achieves awareness of the natural world. There are inevitably hazards from anti-social behaviour and suboptimal use of technology, but the iNaturalist developers are busily refining the mobile app to attract larger numbers of those elusive adolescents with short attention spans. QuestaGame is one experimental interface endeavouring to turn nature-recording into a fun activity – with a competitive edge. Understanding and meeting the needs across a broad spectrum of potential users is critical to the value of citizen science. We analyse >270 topic, place and habitat-based Projects in NatureWatch NZ to see what is attracting people, how systematic acquisition of robust data can be made appealing while satisfying educational, science and government agency standards.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.021 | 0.063 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.066 | 0.035 |
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".