The Mythical element of Margaret Laurence’s “The Stone Angel”
Bibliographic record
Abstract
Margaret Laurence's last four novels are set in her fashioned village of Manawaka, a part of Canada that is based on her own childhood in Neepawa, Manitoba, and it is in these novels that we find the continuous flow of a created world that engenders and cultivates its own legends and mythology.Although each novel contains one set of character who are technically independent of those preceding them, we nevert encounter individuals and families whose stories have been told in previous novels.Since myths in one way or other sharpen or assert reality as writes, "Myth making is a permanent activity of all men" and "all that men can do is to aba myth for the sake of another" (1970, 89 & 92).perspective myth often points to certain time tested beliefs and some universal co-ordinates. INTRODUCTION:An essential myth that Laurence seems to be shaping in her Manawaka novels is that of the family.Tradition has it that all members work collectively and cooperatively and out of this collective effort the individual members will develop and grow.The child was thus prepared for adulthood through the experiences of family solidarity; the son was considered mature when he becomes capable of assuming the role of his male able to assume her mother's maturity was confirmed when she was able to assume her mother's role.Although this description was crude, it nevertheless conforms to the outlines of the nuclear family myth as articulated and refined throughout Western culture, perhaps officially stated at first in the Bible, and certainly perpetuated in endless manner and mode by all Western societies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".