Bibliographic record
Abstract
Thomas King is one of the best-known contemporary Native writers in Canada helping provide Canadian literature with a wide range of cultural backgrounds. His second novel, Green Grass, Running Water deals with several stories about the First Nations people in a Blackfoot community in Alberta, Canada. Among the stories, the life of Lionel is especially worthy of notice with three mistakes that he had made from his childhood to middle-age. The mistakes show that he has imitated white people by disguising his Indian identity with clothes he wears, an effect of colonialist ideology which promotes white supremacy over colored people. His mistakes of the past have a lasting influence on his presence and are related to white people, which justifies the presence and role of trickster characters in the novel. With the help of the trickster, who serves as a saviour or healer in Native American mythology and literature, Lionel successfully finds his right place and purpose in his life by restoring his identity as a Blackfoot Indian. His life suggests the question of the unhealed past and its lasting influence on the present reflecting the fate of the whole aboriginal people. This analysis takes a closer look at how Lionel's masking and unmasking is portrayed as a process of healing and restoration for the whole indigenous community.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".