Chiming the Native Woman and the Sacred Bison: Untangling the Meaning of Mythical Creatures With Respect to Elinor in Lynda A. Archer’s Tears in the Grass
Bibliographic record
Abstract
The defining character of aboriginal cultures is their belief in the sacredness of both the living and non-living things in nature. Myths and legends play an important role in shaping the belief system of these people towards land and its beings. When talking about land, aboriginal women take an upper hand as they are the ones who lives in close proximity with nature, its beings and the mythical stories associated with it. Linda A. Archer’s Tears in the Grass has a ninety-year-old Cree woman as the protagonist. The novel is filled with images and symbols from nature that become the milestone of Canadian native mythology which is long being forgotten by the present generation of Canada. This paper tries to unravel how the aboriginal women imbibe meaning and lessons of life from nature and the related mythology. The paper also uses the Vedic concept of Prakriti to draw this parallel between the native woman and the natural world
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.044 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".