Bibliographic record
Abstract
The pre-colonial Aboriginal communities of Canada have encapsulated their traditional knowledge of survival within a rich repertoire of oral narratives and disseminated it through inter-generational storytelling sessions, across centuries. The facilitators of Euro-Canadian settler colonization have, however, impinged upon indigenous right to self-determinism by denying the truth-claim of this collective memory and forcing their medium of expression (writing) upon their communities. The act of inscribing the traditional ‘oral’ within the folds of the contemporary ‘written’ has therefore emerged to be an effective tool of cultural decolonization for the Native-Canadian populace in the second half of the twentieth century. The conscious inclusion of culture-specific nuances inherent within their languages within literatures penned in the ‘language of the colonizer’ (English) has also come up as an important step towards the reclamation of indigenous autonomy in Canada By delving deep into Ravensong (1993) and Whispering in Shadows (2000) – the two seminal novels written by the storytellers, educators and activists Lee Maracle (Salish) and Jeannette Armstrong (Okanagan) respectively, this chapter aspires to explore the significance of this collective communal memory in building an active resistance against colonial oppression, subvert the systemic and systematic erosion of cultural autonomy and overhaul the forces of political disempowerment that have overshadowed the lived experiences of the indigenous people of Canada for centuries.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".