Using the Phylo Card Game to advance biodiversity conservation in an era of Pokémon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Broader realization of both increasing biodiversity loss and pressures on ecosystems worldwide has highlighted the importance of public perceptions of species and the subsequent motivations towards improving the status of natural systems. Several new proposals have arisen in reference to environmental learning, including mimicking popular gaming media. Inspired by the popular game Pokémon, the Phylo Trading Card Game (Phylo game) is one such emerging possibility. It was invented as an open-source, competitive, and interactive game to inform players’ knowledge of species, ecosystems, and negative environmental events (e.g., climate change, oil spills, wildfires). The game has now achieved global reach, yet the impact of this game on conservation behavior has never been tested. This study used a randomized control trial to evaluate the Phylo game’s impact on conservation behavior (i.e., Phylo condition). This was compared to an information control condition with a more traditional learning method using a slideshow (i.e., Slideshow condition). A second card game was used to control for the act of playing a game (i.e., Projects condition). We found that ecological perceptions (i.e., the perceived relationship of species to their ecosystems) and species knowledge increased after both the game and the slideshow, but the Phylo Game had the added benefit of promoting more positive affect and more species name recall. It also motivated donation behavior in the direction of preventing negative environmental events instead of directly aiding an individual species or ecosystem. Our findings highlight the potential value of this game as a novel engagement tool for enhancing ecological literacy, motivations, and actions necessary to meet ecological challenges.
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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 | 0.000 |
| 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 | 0.000 |
| 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 it