Ned Land et l’utopie compensatoire chez Jules Verne: à propos du Canadien de <i>Vingt Mille Lieues sous les mers</i>
Bibliographic record
Abstract
Jules Verne, qui appelait le Canada ‘mon pays de prédilection’, a écrit trois romans canadiens et donné jour à de forts personnages-types canadiens dans ses Voyages extraordinaires. Le mieux connu de ces personnages est Ned Land, l’intrépide harponneur de Vingt Mille Lieues sous les mers, personnage composite des identités française et anglaise, le Verne de 1869 voyant le Canada de l’immédiate post-confédération comme le lieu de la conciliation franco-anglaise. Ned Land se distingue par son amour de la liberté: au fil des décennies, Verne, endossant désormais les récriminations de l’opinion française contre les ‘Anglo-Saxons’, fera de cette caractéristique celle de tous les Franco-Canadiens, son roman Famille-sans-nom (1889) présentant cette fois l’utopie compensatoire (à savoir cette propension vernienne à faire du Canada le lieu de représentations idéalisées allant à l’encontre de l’histoire événementielle) d’une union entre Canadiens français et peuples autochtones.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".