Bibliographic record
Abstract
This presentation examines the extent of which Nature in Ruby Slipperjack’s Silent Words (1992) serves to reconnect 11-year-old protagonist, Danny, to his Anishinaabe identity. When Danny flees his run-down house in a settler-colonial town, he finds limitless support from the plant and animal life of Northern Ontario. The relationship between boy and Nature transcends the boundary between the human and the more-than-human world and becomes that of a student and teacher. Danny’s reconnection to Nature and his willingness to listen to its many abstract teachings are central to the reclamation of his indigeneity. With the help of some human interpreters, Danny develops the epistemological tools and the humility to allow Nature to heal his past traumas as well. The Anishinaabe medicine wheel teachings profess that a holistically healthy person seeks to find balance among their intellectual, spiritual, emotional, and physical self. Danny achieves this on his journey through the woods while decolonizing and re-indigenizing himself. This reading of the role of more-than-humans in Silent Words also identifies Nature’s propensity to share Anishinaabe teachings in subtle and unexpected ways for those who are willing to listen. Though it is a fictional text, the transformative learning and healing processes Danny goes through after reconnecting with Nature are generalizable to the real-world. In many ways Danny’s reclamation of his Indigenous identity mimics the large-scale Indigenization movement happening throughout Turtle Island today.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".