“WALGAJUNMANHA”: STORYTELLING AND INDIGENOUS CULTURAL RESURGENCE
Bibliographic record
Abstract
Historically, Indigenous cultures have been rendered “transparent” (Byung-Chul Han 2014; Di Blasio 2020) through oppressive legal mechanisms such as the “terra nullius” doctrine in Australia and the 1876 Indian Act in Canada, whose effects persist up to the present time. These frameworks have denied Indigenous existence itself, contributing to systemic marginalization. However, Indigenous narratives have become vital to the decolonization process, both locally and transculturally, fostering the preservation and regeneration of Indigenous languages and knowledge systems. This study examines the role of storytelling, understood in a broad Indigenous sense, in cultural resistance and resurgence by analyzing the works of contemporary Indigenous poet Charmaine Papertalk Green in dialogue with other textualities. Through literature, it identifies recurring themes and discursive strategies employed by Indigenous artists to contest colonial narratives and assert collective identity and memory. Drawing on an interdisciplinary framework that integrates literary studies, postcolonial theory, and Indigenous studies, this paper contributes to understanding Indigenous literature as a political and cultural practice of resurgence. In a global context where Indigenous peoples continue to struggle for recognition and historical justice, the analysis of Indigenous literary production offers crucial insights into ongoing decolonization processes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".