Environments of memory : bio-geography in contemporary literary representations of Canada and the Great War
Bibliographic record
Abstract
Canadian remembrance of the Great War (1914-1918) in the early twenty-first century is often associated with grand gestures at national monuments like the opening of the new Canadian War Museum in 2005 and the restoration of the Vimy Ridge Memorial in 2007. However, these sites of memory, what Pierre Nora terms lieux de mémoire, are not part of the everyday environments of memory, the milieux de mémoire, of most Canadians. In my investigation of three contemporary works of Canadian literature: The Danger Tree by David Macfarlane, Broken Ground by Jack Hodgins, and Unity (1918) by Kevin Kerr, locally-based storytellers describe the continued influence of the Great War on their individual Canadian communities. The fictionalized narrating personas in these three works create what I refer to as bio-geographies: first-person accounts of the narrator’s particular social and memory environments. While the bio-geographers in these three texts lack first-hand experience of the Great War, their writing reflects the continued repercussions of the conflict in the weeks, years, and decades after the 1918 armistice. The Great War differentially affected thousands of communities in Canada and Newfoundland. Constructing a coherent national narrative that accounts for the multiple lived experiences of individuals in communities across North America is virtually impossible. Turning to local representations of the Great War (in the case of the three bio-geographic texts: depictions of communities in Newfoundland, British Columbia, and Saskatchewan) provides a sense of the nation as a diverse landscape of memory with multiple vantage points. Negotiating the complex terrain of self, place, and memory, the bio-geographers in the three works I examine create representations of the past that reveal how sites of memory, lieux de mémoire, come to be firmly embedded in the ongoing lived experiences of comunity members, the milieux de mémoire.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.050 | 0.063 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".