Climate change games literature review: Report on work in progress
Bibliographic record
Abstract
Claims about the ‘power’ of games and simulations to slow the speed of climate change are sometimes exaggerated. We have therefore embarked on a literature review of academic publications on the topic of simulation/gaming and climate change. In our presentation, we will summarize the work so far.As we write this abstract, we have identified over 400 items, some published in peer-reviewed journals, some in games conference proceedings, some in magazines or blogs. We will therefore need to design careful inclusion/exclusion criteria, to have a pool of from 20 to 40 publications.The types of publications vary widely: research on a particular game, tips on facilitating, overview of the role of games in climate education, use of simulation as a climate research tool, role-play of climate negotiations, necessity of debriefing, evaluation of climate games. Types of games mentioned or examined also vary: board, online, computerized, single player, interactive, video, escape rooms and even gamification.We hope that our review will be able to reveal a variety of elements, such as:the real potential and limits of games to influence climate education; how exaggerated the claims are about the power of climate games; what aspects of global warming are present in game publications and in games, e.g., carbon cycle, how GHGs actually 'heat' the planet, floods, hurricanes (and why), slowing of AMOC, role and acidification of the ocean; the use of debriefing in climate games. Our aim is not to propose a taxonomy of climate, althiough categories of climate game types may emerge in the literature. [Please note that most ‘games’ related to climate change education are in fact simulations, often with game elements. In keeping with the long tradition of simulation/gaming (dating back to the early pioneers, such as Duke, Greenblat, Guetzkow), we use the term game to refer to the whole spectrum of interactive events (Ken Jones’ term), from 100% simulation to hybrid simulation/games.]If you are interested in contributing to this work, please come to see us at our poster, or contact us here oceans dot climate at gmail.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".