Harnessing the potential of simulation and speculative games for transdisciplinary collaboration:lessons from experience
Bibliographic record
Abstract
Addressing societal challenges requires taking a systems perspective, and establishing a shared understanding and mutual learning. This includes not only learning and gaining a systemic understanding about a system’s complexity, but also of the perspectives and values involved in challenges to be addressed. In this study, ‘speculative games’, games that focus on hypothetical scenarios or experiencing the consequences of technological, social, or environmental changes, are proposed as extensions of simulation games as tools to explore perspectives and values. By reflecting on experiences of the design and use of three games, we provide preliminary insights into the benefits of both simulation and speculative games. We discuss how the speculative games, in contrast to the simulation game, use deliberately open-ended objectives and ambiguous in-game objects and materials to contribute to issue formation. We further discuss how, as a result, the speculative games establishes mutual learning through collective sense-making of the games’ ambiguous materials and reflecting on how these relate to real-world issues. In the simulation game, learning both about the system’s complexity and the perspectives of other players originates from experimenting, discussing and reflecting on actions taken in the game. Our experiences suggest that simulation and speculative games can be complementary tools in addressing societal challenges. As these type of games are not mutually exclusive, future research can focus on exploring the use of speculative elements in simulation games that aim to facilitate transdisciplinary collaborations and addressing societal challenges.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".