Bibliographic record
Abstract
Abstract When both England and France began colonizing the eastern part of North America in the seventeenth century, it was inevitable that their centuries-old European rivalry would manifest itself in the western hemisphere as well. The French in Canada and the English along the east coast of North America remained isolated from each other for about a century, but in the early 1750s the English colonists pushing west confronted French forces moving south into the area just below the Great Lakes Ontario and Erie. At the junction of the Allegheny and Monongahela rivers English colonists from Virginia began claiming land by constructing a fort. Before it was completed it was captured and strengthened by French and Indian forces who named it Fort Duquesne. A Virginia militia attempt to retake the fort in 1754 (led by George Washington in his first command) failed in its mission. After some 150 years of salutary neglect, the London government committed troops to the colonies in an attempt to beat back the French incursion. Major General Edward Braddock’s they had been abandoned by their Indian allies and numbered but 600 soldiers, marines, and Canadian militia. They rushed the English position but were blasted by seven volleys of English fire, then harried on their retreat by the hidden Iroquois.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.062 |
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".