Beyond the “Historical” Simulation: Using Theories of History to Inform Scholarly Game Design.
Bibliographic record
Abstract
The authors of this paper present a case for a gamic mode of history that focuses on the construction of the historical narrative via procedural rhetoric. The gamic mode of history presented in the paper maintains the constructionist epistemologies and explanatory narratives for the creation of reasonably justifiable truths found in many current text based works of scholarly history. It maintains them yet changes the mode to an interactive digital form where the reader explorers the historical argument through meaningful decision making and play. This paper establishes that the epistemologies of constructionist history are not mode dependent which allows for a change of mode without a change in epistemology. This is different from some other recent explorations of digital forms of history where in the pursuit of historical accurate reconstructions of the past researchers fail to address how we construct knowledge about the past, or assume that how we know the past much change with the mode of expression. It is suggested by this paper that in the gamic mode it is not the historical past and accepted epistemologies one should reconstruct, but simply the mode of the historical narrative about the past.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".