Decolonizing “Allyship” for Indian Country: Lessons from #NODAPL
Bibliographic record
Abstract
Abstract In 2016, when #NODAPL first appeared in the mainstream media, many nonnative people approached me about how to support the water protectors. This question can be answered in a couple of ways: first, I might address the specific issue (actions that directly support those at Standing Rock), or second, I might respond more generally about how to be an ally to native people. The two responses highlight a current issue in Indian Country: should nonnatives serve as active bystanders—or should they be allies to native peoples? Being an ally has come under scrutiny, especially given its propensity for epistemic injustice. Some philosophers—such as Rachel McKinnon—argue for dismissing the concept altogether, requiring that individuals serve as active bystanders. Although this may be necessary to support individuals in the transgender community, it lacks the resources to fully address the needs of colonized peoples. In this article, I argue for the operationalization of “ally” in Indian Country insofar as it is subject to decolonizing treatment. Although there is a need for both bystanders and allies in Indian Country, the Indigenous people must define the concepts that are intended to serve them.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".