Bibliographic record
Abstract
Ask the average person on the street about the Seven Years' War and you are likely to get a blank stare. Try again, only this time call the conflict "The French and Indian War" and you might get a faint smile of recognition. Take a different approach: ask random strangers their opinion about The Last of the Mohicans. Many will tell you they loved it, although they will more likely be thinking about Daniel Day-Lewis than James Fenimore Cooper. Such has been the fate of one of the most important events in early history. In 2004, the 250th anniversary of George Washington's surrender at Fort Necessity passed quietly, recognized mostly by historians, reenactors, and local institutions in southwestern Pennsylvania already familiar with the story. A year later, the anniversary of Braddock's Defeat passed under similar circumstances. The coming years will bring similar anniversaries at places whose names evoke North America's colonial past: Ticonderoga, Niagara, Louisbourg, and Quebec. Museums, historical societies, and various other organizations have launched symposia, conferences, and exhibits to honor the occasion, and there is even a PBS television production scheduled for broadcast in early 2006. But no single event commemorating the 250th anniversary of the Seven Years' War in America is likely to capture national interest in the way the Bicentennial did in 1976 or Ken Burns's Civil War series did in 1990. [excerpt]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".