Bibliographic record
Abstract
This research paper attempts to underscore the growth of the Canadian personality reflected in The Stone Angel. The Canadians' psychological sufferings are largely caused by their country's subordinate position under the imperial power of America. In Canada, the citizen who is trapped between the American technological superpower with its spiritual poverty on the one hand and his own psychological unrest on the other fails to establish a workable balance between his needs and interests and the society's values and expectations. This "colonial mentality" prevents the Canadians from valuing themselves. They withdraw from reality into their inner world and cannot act because they see themselves as acted upon. Consequently, they accept to play the passive role which is extended by their self-conceit. The Canadian citizen who is victimized by different visible and invisible forces is psychologically disturbed, insecure and frustrated. In The Stone Angel, Margaret Laurence tries to diagnose and analyze the Canadian characters' psychic conflicts within their social and political framework. Furthermore, she investigates in the consciousness of the characters' personal life to study their relations to each other and to examine their potentiality. Laurence tries to help Canadians create a more positive identity, for she strongly disapproves of the negative destructive self-image created by the Canadians themselves and tries to rediscover their authentic selves.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".