Bibliographic record
Abstract
The article is grounded in the premise that the historical narrative in Canadian literature strives to either forge a unifying national myth or deconstruct it. It opens by discussing two examples of historical narrative in the form of the long poem. John Newlove historicizes the Native American mythology that he deploys by adopting the perspective of historical accounts (by G. Hyde and D. Thompson) and by referring to historical events (the North-West Rebellion). E. J. Pratt attempts to create a national mythological narrative in his renowned long poems Brebeuf and His Brethren and Towards the Last Spike . Although he extensively paraphrases and quotes the historical sources on the subject, they are unobtrusively woven into the narrative. Pratt thus conceals the fictional additions and the omissions of historical references, and consequently constructs a bicultural national myth at the expense of suppressing the perspective of the indigenous peoples as well as the historical trauma of French Canadians. Following the 1960s, when Canadian nationalism reached its apex and the national interpellation was dispersed into various cultural institutions, fiction lost momentum as a creator of national myth. For example, an analysis of Leonard Cohen’s Beautiful Losers and Rudy Wiebe’s The Scorched-Wood People and The Temptation of Big Bear shows that both authors employ a narrative strategy comparable to the one used by Pratt by extensively paraphrasing and quoting historical sources, but the effect is rather to deconstruct the existing national myth into a play of various possible histories (Cohen), or to construct an alternative, previously suppressed version of history (Wiebe). The article argues that despite the change in the function of the narrative strategy, Pratt may be seen as one of the sources for the use of paraphrasing and quoting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".