Bibliographic record
Abstract
ONE OF THE persistent myths about Canada is that it is a nation of two solitudes, a country that is divided between the British and the French. As M.J. Toswell and Anna Czarnowus explain in the introduction to this volume, the majority of literary medievalism produced in pre-Confederation Canada drew on the histories and cultures of Britain and France. Yet, as is often the case, the exceptions help explain the rule. The imagined medieval ancestors of early Canadian literature did not always match the demographics of the nation: for example, Scandinavia (and thus Canada's imagined Viking heritage) was a popular topic long before the discovery of L’Anse aux Meadows or substantial Scandinavian immigration to Canada. Even more surprisingly, early Canadians loved to imagine the Middle East. Nineteenth-century English Canadian periodicals published more than a hundred essays, stories, and poems inspired by the region, yet at no point in the century did Canada have a sizeable population with direct connections to the Middle East. This literary relationship was thus one-sided, reflecting desires not to engage with another part of the world, but rather to consume a fantastic, home-grown Orient. The vision of the region that these authors created and disseminated to the English- Canadian public was one that was simultaneously alluringly exotic and dangerously Other – though they kept this danger at a comfortable distance. In some cases, they created distance through genre: romance was particularly popular, perhaps following English author Clara Reeve's 1785 model of romance as something that “describes what never happened nor is likely to happen” to appeal to a predominantly white, Anglo-European settler audience that did not expect direct contact with real Middle Eastern cultures. Other stories accomplished distance through time. Parallel to and reinforcing early Canadian interest in romantic Orientalism ran an even deeper thread of fascination with the medieval world; Canadian periodicals regularly presented their audiences with stories set in both exotic times and places. This fascination was prodigious: to date, I have catalogued more than nine hundred examples of English-Canadian medievalism published in Canada between 1789 and 1900; of these, ninety-four engage with the histories, literatures, or cultures of the medieval Middle East.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.015 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".