Storying presence : Aboriginal literature, critical strategies, and Eden Robinson's Monkey Beach
Bibliographic record
Abstract
"Storying Presence: Aboriginal Literature, Critical Strategies, and Eden Robinson's Monkey Beach" is an examination of some of the many issues that have emerged in current discussions of Native literature and an interpretation of how they relate to Eden Robinson's highly successful Monkey Beach (2000). This project first examines and reviews the current criticism on Monkey Beach and argues that critics have largely evaluated the novel with terms and concepts that emphasize Native identity questions in the text. Moreover, these critics formulate identity questions in language that draws on a dichotomy of civilization and savagery. Gerald Vizenor's theories of deconstruction draw attention away from identity questions and instead shed light on ways in which Robinson builds relationships between her characters, examines human potential for violence, and makes use of humour. Robinson creates a narrative of what Vizenor calls survivance by refusing to imbue her characters with identifiable cultural markers, thus stretching what readers might imagine are the borders of Native cultures. However, Money Beach simultaneously refers to a distinctly Haisla epistemology, and, thus the novel must also be interpreted using an indigenous approach that highlights the relationship between the novels' characters and the land. Although postmodernist and indigenist approaches are in many ways opposed, Robinson uses them in conjunction.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".