"Other languages, other landscapes, other stories": Reading Resurgence in the Contemporary Indigenous Novel
Bibliographic record
Abstract
As settler and postcolonial countries in North America, Oceania, and South Asia contend with the complexity of reconciliation, sovereignty movements, and the fallout from colonial schools, the relevance of Indigenous resurgence is rising on a global scale. This resurgence responds, in part, to the specific role literature can and has played in disconnecting Indigenous Peoples from their knowledges, communities, and selves. Accordingly, in this dissertation I make connections between seemingly disparate Indigenous novels in an effort at beginning to understand what representations of resurgence—the everyday practices and processes that seek to regenerate and rebuild Indigenous nations—reveal about how diverse Indigenous contexts are (re)imagining Indigenous worlds and what connections across those contexts might mean (Simpson 2017). To perform this investigation, I make a case for further cross-cultural comparative methods within Indigenous literary studies that can interpret resurgence across distinct literary contexts while maintaining a commitment to nation-specific worldviews imparted by relation with land. \n\nMobilizing the theoretical work of Leanne Betasamosake Simpson (Michi Saagiig Nishnaabeg), Chadwick Allen, and Molly McGlennen (Anishinaabe), this project contributes a new comparative method called reading resurgence. Located at the intersection of global and nationalist approaches to Indigenous literary studies, this method interprets everyday acts of resurgence—specifically: storytelling, language learning, and relationship with land—trans-Indigenously across three respective literary constellations of coresistance that cluster novels from diverse Indigenous nations. The first constellation reads resurgence across David Treuer’s (Leech Lake Ojibwe) The Translation of Dr Apelles (2006), Patricia Grace’s (Māori) Potiki (1986), and Rejina Marandi’s (Santal) Becoming Me (2014). The second clusters Cherie Dimaline’s (Métis) The Marrow Thieves (2017), Sia Figiel’s (Samoan) Where We Once Belonged (1996), and Easterine Kire’s (Angami Naga) Don’t Run, My Love (2017). The third reads across Eden Robinson’s (Haisla & Heiltsuk) Monkey Beach (2000), Kiana Davenport’s (Kanaka Maoli) Shark Dialogues (1994), and Mamang Dai’s (Adi) The Black Hill (2014). Beyond its methodological contribution, this dissertation is also an effort to advance scholarly understandings of how contemporary Indigenous novels are (re)connecting Indigenous Peoples and nations with traditional ways of being and knowing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".