Beyond the “Babel Problem”: Defining Simulations for the Social Studies
Bibliographic record
Abstract
Simulation research has become a growing area of interest in the social studies in recent years. Problematically, the term simulation is used without consistency among practitioners and researchers. The conceptual confusion regarding what simulations are (or are not) muddies the field and makes it difficult for scholars to make sense of this phenomenon or to talk about simulations across findings. In order to bring clarity to the field, this paper is framed around two conceptual and analytic constructs: conceptual analysis and the theory of language games. In this paper, I will provide a rationale for why the social studies field requires a specific definition for simulations. Next, simulations will be defined using four specific criteria: verisimilitude, dynamism, active human agents, and pedagogical mediation. Finally, simulations will be differentiated from three related phenomena with which they are often conflated: games, role-plays, and models.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".