Trauma, Resonances, and Transformations: Gaming as Heuristic Mode for Doing History
Bibliographic record
Abstract
This paper examines the potential of video games as a heuristic tool for engaging with history, particularly in the context of traumatic events and contested narratives. Traditionally, video games have been dismissed as trivial entertainment, unsuitable for addressing complex historical topics. However, new paradigms challenge such perceptions by exploring how ludonarratives – stories shaped by game mechanics –can facilitate transformative learning. By shifting players from passive spectators to active participants, games offer immersive experiences that can encourage critical engagement with historical events. The emotional impact of these experiences, supported by empirical studies, has the potential to promote empathy, understanding, and social change. Building on theories of resonance and transformative learning, this contribution advocates for a re-evaluation of video games’ role in historical education, emphasising their ability to provide meaningful, multi-perspective experiences that deepen our understanding of the past and its relevance to contemporary issues. Keywords: gaming, ludonarratives, transformative learning, emotions and affect, resonance
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".