The Cultural Politics of Renaming in Selected Native Canadian Poems
Bibliographic record
Abstract
Cultural erasure and land dispossession form the core of the settler colonization of Turtle Island (Canada), a colonization thatseeks the production of colonial space through processes such as colonial naming which aims at erasing the presence of the Indigenous peoples, invalidating their cultures and claiming ownership over their lands. In their resistance to their cultural and physical eradication and the occupation of their land by the settler colonizers, the Indigenous writers adopt the revitalization of their languages as an anticolonial discourse of Indigenous cultural preservation. Many past studies investigated the decolonization processes in Indigenous Canadian poetry. However, renaming as a decolonizing act of Indigenous cultural revitalization is notapproached in the selected poems. Thus, this article examinesthe renaming practices in Duncan Mercredi’s “mahikan”(1991), Marilyn Dumont’s “nomenclature” (2007)* and extracts from Louise Bernice Halfe’s book-length poem, Blue Marrow(1998). The article applies the ideas on naming expressed in Linda Tuhiwai Smith’s Decolonizing Methodologies: Research and Indigenous Peoples(2012), as well as the concept of ‘generative refusal’ which is conceptualized by Leanne Betasamosake Simpson in her book As We Have Always Done: Indigenous Freedom through Radical Resistance(2017). The article concludes that renaming as it is adopted by the selected poets serves as a counter-narrative thatunsettles the colonial discourse of Indigenous cultural erasure and land dispossession by reasserting the presence of the Indigenous peoples and their spiritual connections to their land and culture.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.041 | 0.035 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".