Speaking truth to power: Indigenous storytelling as an act of living resistance
Bibliographic record
Abstract
In our preparation for this issue, we had particular expectations and beliefs about what it meant to theorize and map out decolonization. We saw decolonization as under theorized and needing more attention. What the authors of this issue reminded us of is that decolonization does not fit the demands and expectations of the Western Euroversity – it is alive and vibrant, being theorized and enacted in Indigenous communities around the globe through practices such as story telling. In this editorial we examine the role that Indigenous storytelling plays as resurgence and insurgence, as Indigenous knowledge production, and as disruptive of Eurocentric, colonial norms of ‘objectivity’ and knowledge. As the authors in this issue explore the specific and located knowledges that work to decolonization, we finish by asking what the role of the reader is in bearing witness to these profound, powerful, and complex articulations of decolonization and Indigenous being.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".