The 1759 Campaign for Quebec City: A Historical Wargame of the French and Indian War
Bibliographic record
Abstract
The British campaign to capture the French fortress city of Quebec in 1759 led to the decisive battle of the French and Indian War, and paved the way for British domination of North America for the next twenty years. While the ultimate battle on the Plains of Abraham is known to most scholars, the campaign along the St. Lawrence River that led up to the battle provides many lessons of operational level leadership that are still relevant today: maneuvering forces and securing lines of operation, integrating land and naval forces in joint operations, massing forces at the decisive point, simultaneity in operations, and the strategic use of key terrain. This paper provides the historical basis and rationale for the modeling decisions made by the researcher in the development of this wargame. Players will take on the roles of the Army Commanders, both learning the historical details of the campaign, and immersing players in the commanders' decision making process. The wargame enables players to make decisions consistent with the information and capabilities available to the commanders during the campaign, and thereby allows them to gain an understanding as to why the campaign unfolded the way it did.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".