Grim FATE: Learning About Systems Thinking in an In-Depth Climate Change Simulation
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
Background. Simulations of complex systems have a long history of use for the study and promotion of systems thinking , yet more can be done in identifying games that promote development of systems thinking . Aim. This study is an exploration of the hypothesis that FATE OF THE WORLD, a challenging and complex climate change simulation , can promote systems thinking about climate change. Questions. This article analyzes players’ engagement with FATE OF THE WORLD using three key questions : 1. In what ways does the game support thinking about climate change as a complex system? 2. Does the game correspond to players’ a priori model of climate change? 3. How do players relate to FATE as an artifact they embrace, critique, and tinker with? Method. 33 participants were matched into control and test groups , and experimental participants were assigned to play a full game scenario of FATE OF THE WORLD. Experimental and control groups were compared using pre-and-post intervention concept maps . Post interviews were conducted with the test group. Results. Concept maps revealed statistically significant differences between the control and test groups. Interviews revealed diversity in learning outcomes and the ways in which acceptance of the game’s model of climate change influenced learning. Conclusions. FATE serves as proof-of-concept for the power of complex simulations to promote systems thinking as well as in-depth reflection on key social challenges . However, simulations like FATE are unlikely to serve well as stand-alone educational tools, which highlights the importance of effective teaching to accompany the game.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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