Bibliographic record
Abstract
Two classroom simulations—SUPERPOWER CONFRONTATION and MULTIPOLAR ASIAN SIMULATION—are used to teach and test various aspects of the Borden versus Brodie debate on the Schelling versus Lanchester approach to nuclear conflict modeling and resolution. The author applies a Schelling test to segregate high from low empathic students, and assigns them to hard case positions in three simulations to test whether high empathy students can engage in tactic bargaining and whether low empathetic students are necessarily as escalation prone. He has a bipolar nuclear simulation that is an easy case for the Brodie set of assumptions about nuclear war, avoidance, and Schelling-esque tacit bargaining. He expects the system structure and high empathy leader selection to contain escalation, despite the temptation of relying on accelerated Single Integrated Operational Plan solutions and the counterincentive of diminished tacit bargaining through decapitation attacks. The second simulation is a multipolar nuclear simulation set in the near future of Asia, and emulates the Borden-esque logic of nuclear war as artillery exchanges, with a Lanchester square law logic encouraging rapid escalation, coupled with a selection for the most autistic leadership. The author expects rapid nuclear escalation under these structural and decision-making conditions. His conclusions are anecdotal, but seem to indicate, from student feedback during class discussions, that the failure to model fear may be a factor in undermining successful tacit bargaining by players, suggesting that Borden rather than Brodie better conceptualized nuclear conflict. Therefore, peace is about restraining war initiation, as there are great pressures for escalation once war is initiated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".