Bibliographic record
Abstract
This study highlights the significance of literature to Expo 67 and the tradition of world exhibitions. Like the world’s fairs before it, the Montreal exhibition promoted literature as a technology of national progress. Yet Expo 67 reflected simultaneously the contemporary anxieties about technology and nationhood expounded by George Grant in the 1960s in contrast with the celebration of nationalism and technological progress bound together in the tradition of world’s fairs. Though world’s fairs had depicted the educational utility of literature throughout their tradition, Expo 67 emphasized literature more than any world exhibition had to that point. Its theme, Terre des hommes, drawn from Antoine de Saint-Exupéry’s autobiographical narrative, its literature conferences and poetry performances, and the broader textuality of Expo 67 as a “total environment” were intended to deploy texts in promotion of the ideals of universal humanism. However, I find that poetry performances by Michèle Lalonde, George Clutesi, and Duke Redbird contradicted the event’s stated ideals by spotlighting the negative effects of technology and ongoing colonial violence. I argue further that large-scale literature exhibits at Expo 67 resisted the humanist ideals of the event by continuing the world exhibition convention of treating literary works as an educative technology of national progress. As a result, I find that Canadian literature at Expo 67 complicates the Canadian collective memory of the event and the 1967 Centennial Year.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.027 | 0.029 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".