Le récit de filiation dans l’œuvre de Nancy Huston: mémoire et identité de "Cantique des plaines" à "Bad Girl"
Bibliographic record
Abstract
Le recit de filiation dans l’œuvre de Nancy Huston: memoire et identite de Cantiques des plaines a Bad Girl L’analyse se propose d’enqueter le role que la memoire intergenerationnelle et identitaire joue dans l’oeuvre romanesque de Nancy Huston a la lumiere de son dernier recit autobiographique, Bad Girl , qui raconte la saga d’une famille de la middle class canadienne dans les annees 50 du point de vue du foetus que Nancy Huston a ete. Cette perspective confere des enjeux nouveaux, que l’analyse mettra en relief, a la relation mere-fille et a la culture familiale et nationale. The Tale of Filiations in Nancy Huston’s Work: Memory and Identity from Plainsong to Bad Girl The analysis will investigate the role that inter-generational memory and identity play in Nancy Huston’s works in light of her latest autobiographical novel, Bad Girl , which narrates the story of a Canadian middle class family in the 1950s from the point of view of the foetus that Nancy Huston was at the time. This perspective creates new issues that the analysis will highlight, in the mother-daughter relationship, family and national culture. Il racconto di filiazione nell’opera di Nancy Huston: memoria e identita da Cantique des plaines a Bad Girl L’analisi si propone d’indagare il ruolo che la memoria intergenerazionale e l’identita giocano nei romanzi di Nancy Huston alla luce dell’ultima autobiografia, Bad Girl , che racconta la saga di una famiglia della classe media canadese degli anni cinquanta dal punto di vista di Nancy Huston nel feto. DOI: 10.1400/247561
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".