‘Stories To Stay, Stories To Subvert’: The Role of Collective Communal Memory in the Native-Canadian Struggle for Resistance against Colonization
Bibliographic record
Abstract
The indigenous communities of Canada have transmitted their traditional knowledge of survival from one generation to another through oral storytelling sessions since the pre-colonial times. This knowledge has remained encapsulated within their collective communal memory in the form of stories of ancestors, tales of tricksters, dream-vision narratives, ceremonial songs, and ritualistic recitals. But forces of Euro-Canadian colonization have encroached upon their right to autonomy through a coercive imposition of the colonizers' language (English) and the colonizers' medium of expression (writing) upon them. The starkly different consciousness of ‘history’ that governs the worldviews of the dominant and the dominated have only served to aggravate the imbalance of power even more. The late twentieth century has seen the literary productions of these communities’ strife to reclaim their cultural and thereby political autonomy by inscribing the ‘oral’ within the ‘written’ and reworking the semiotics of the foreign tongue, imposed upon them to incorporate the specific nuances of their traditional language-culture within it. By looking into Ravensong (1993) and Whispering in Shadows (2000) penned by writer-activists Lee Maracle (Salish) and Jeannette Armstrong (Okanagan) respectively, this paper aims to explore the subversive potential of this collective cultural memory in resisting the colonial atrocities, the erosion of identity and the political disempowerment that has plagued the Native-Canadian existence 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.045 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".