Bibliographic record
Abstract
If asked to identify a medieval landscape, no one's first thought would be Toronto, Ontario.But if you are a day visitor to the city, there are at least two attractions which will try to sell you a 'medieval' experience. 1 Visitors could take in some sightseeing at Casa Loma, Sir Henry Pellatt's early twentieth-century creation, 'Toronto's Camelot' .Constructed 'with soaring battlements and secret passageways' , it 'paid homage to the castles and knights of days gone by' . 2 After a day spent at the castle, attention turns to Medieval Times Dinner and Tournament, where patrons enjoy a joust, some falconry and eating their meal with their hands.This venue promises a journey through time: Travel through the mists of time to a forgotten age and a tale of devotion, courage and love -at Medieval Times Dinner and Tournament.Imagine the pageantry and excitement that would have been yours as a guest of the royal court ten centuries ago.That's exactly what you will experience at North America's most popular dinner attraction.3 While neither attraction refers to actual medieval artefacts or history (they don't commemorate a well-known historic event or necessarily house any medieval artefacts), they create two enclaves of the 'medieval' right in the middle of Canada's largest city.Both Casa Loma and Medieval Times offer visitors a chance to see and engage with commonly accepted signifiers of the 'Middle Ages' , like jousting or castle crenellations.The average North American, who lives nowhere near the abbeys, cathedrals or geographic locations traditionally associated with the Middle Ages, can still often find a 'medieval' landscape close by -a space that has been coded for the tourist as a 'medieval' space.Through the tourist gaze, a 'medieval landscape' takes shape.Ongoing popular interest in the Western European Middle Ages (despite the inconstancy of that concept), particularly from the second half of the twentieth century onwards, has resulted in a plethora of locations where one could go to meet the 'medieval.' Ewa Skowronek et al. have recently worked to determine what is meant by a 'tourist landscape' , acknowledging that academics use the term amorphously, depending on the aims of their research.4 Part of the difficulty is there are many definitions for even the concept of a 'landscape' .5 Ewa Skowronek et al. come to the conclusion that
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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 | 0.000 | 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 | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".