Bibliographic record
Abstract
In 1950, Anne Hebert published Le Torrent, a collection of seven short stories. These stories containing many shocking themes and expressions have placed her one of the pioneers of modern novels in Quebec. This paper tries to analyze several phases of alienation described in the novels and the reactions of alienated caracters in their situation. Some examples of alienated and mentally or physically deformed characters in Le Torrent are Francois, Stephanie, Stella, etc. Although the author wanted readers to interpret these characters on ther individual level, this paper interprets them differently. The result of this study is as following. Alienation doesn’t come from one’s interior but his exterior. Society and history are major agents of alienation. The injustice of life imposed on the caracters results from political and religious underdevelopment, cultural lowness, absence of social security system and of universal education at that time. The conquest of Quebec by England left a deep and historical wound on the French Canadians. This fact is, in my opinion, one of the essential themes of Anne Hebert’s novels. In spite of all these alienating situations, the reactions showed by the caracters of the novels are limited to escapist illusion, self-destruction, mistaken revenge, eternal submission, etc. In conclusion, Le Torrent by Anne Hebert which deeply approached themes of violence and alienation could be called authentic landscape of the inner world of Quebecois before ‘la Revolution tranquille.’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".