Bibliographic record
Abstract
Few settings are as quintessentially American as the Wild West. In games, the myth of the American frontier is the myth of American expansion. This myth explores, sometimes with enthusiasm, at other times with a tinge of regret, the conquest of the “wilderness” with its Native inhabitants and wildlife, and its replacement by “civilization” represented by settlers and railroads. Yet, European expansion on the frontier is not exclusively an Anglo-American story. It was the French explorers, traders, and trappers that first set out westward (and southward) along the rivers from Canada and the Great Lakes. The French experience of the frontier was radically different: for them, with the limited resources of their soon-to-be-sold colonial empire, the wilderness was effectively untameable, its Native inhabitants unconquerable: it was thus a place of permanent danger, where one might, with equal probability, eke out a living, earn a fortune, or simply perish. Only once has the French West appeared in a digital game, in Silmarils’s Colorado (1990). This paper examines Colorado as an artefact of French game development in the 16-bit era, as a unique depiction of the forgotten French West, and, finally, as a 2D predecessor of today’s sprawling 3D open-world games.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".