Using the Phylo Card Game to advance biodiversity conservation in an era of Pokémon
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".