EcoDragons: A Game for Environmental Education and Public Outreach
Bibliographic record
Abstract
Environmental education is crucial to tackling the pressing ecological and societal issues on our planet. Although there are various ways to approach environmental education and raise public awareness, games are potentially an effective vehicle of knowledge and engagement because they vulgarize the scientific information in a universal 'language' and bring people together. Here, we designed a game, EcoDragons, that integrates principles of ecology, biological conservation, life history, and taxonomy. The protagonists of the game are dragonflies and damselflies. The aim of the game is to colonize habitats with different species and use ecological processes (e.g., predation, competition, and mutualism) and conservation measures (e.g., restoration and reintroduction) to face random environmental disturbances (e.g., climate warming, drought, pollution, and biological invasion). The version of the game presented in this paper was based on European species. The game includes 50 species (25 dragonflies and 25 damselflies). The winner of the game is the one who occupies more habitats, establishes and maintains the largest number of species, and solves more anthropogenic disturbances. EcoDragons has a global outreach potential to educate the public about ecology, conservation, and organismic life history, and will probably engage people in environmental advocacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".