METANARRATIVES AND TEXTUAL IRONIES IN ROBERT KROETSCH'S GONE INDIAN UDC 820(71).09-31
Bibliographic record
Abstract
Abstract. The narrative structure of Robert Kroetsch's novel Gone Indian is multi-layered and abounds in the use of mythological fragments with special reference to the myths and stories of the Canadian past and present. Robert Kroetsch's use of myth in his novels, as well as in the novel Gone Indian, is exposed to parody and irony for he believes that familiar myths and stories must be "deconstructed", "uninvented " and "unnamed " (all his favored terms) as fictions. The surface story of the novel Gone Indian is concerned with the issues of identity of the main hero Jeremy Sadness whose life story, recorded in fragments on the tape re-corder, is manipulated and interpreted by another narrative voice in the novel, Jeremy's professor and supervisor, Mark Madham, who plays the role of a parodic reader-surrogate and writer-surrogate. What is of interest here is not the overt structure and content of the narrative, but the covert structures, hidden in "the space between ' the text and the actual reader. Three of the most significant texts beneath the surface story of Gone Indian are: Frederick Jackson Turner's notion of the American frontier; the story of "Gray Owl", a type of an ironic trickster figure in the text; and Jack Shadbolt's mural at the Edmonton
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".