Unlikely Thespians: The Historical Re-enactors of the Royal George Society
Bibliographic record
Abstract
On a sunny June weekend in 1985, the sparkling waters of Georgian Bay off Midland, Ontario, were full as usual of powerboats and sailing yachts, coursing the waters of one of the finest boating venues in Canada. Cleaving through the middle of their clustered white fibreglass hulls was a dark little wooden schooner of early-nineteenth-century rig, its tan canvas heeling it over in a fresh breeze as it tacked in incongruous Georgian dignity through the wakes of thundering “cigarette boats” and gleaming sailing machines. The schooner was HMS Bee, a replica of a British naval supply schooner of the year 1817, maintained at a nearby historic site by the Province of Ontario. Crewing it were men and women in the quaint, anachronistic garb of Regency-era sailors, as different in their canvas duck trousers, billowy shirts and sensible straw hats from the bikini-clad passengers of the hurtling powerboats as it is possible to imagine. The Bee’s crew were the first members of a newly formed society of historical re-enactors with the ponderous title of “Ship’s Company and Landing Party, His Britannic Majesty’s Ship Royal George.” They were another manifestation of a distinctly North American and European phenomenon: the hobbyists who seek to recreate historical times by placing themselves within those times as accurately costumed and equipped participants.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".