Looking Beyond Binaries: How Native Activists Create Decolonized Futures
Bibliographic record
Abstract
Native people in the United States and Canada have been resisting settler colonialism for as long as settlers have tried to impose it upon them. That activism has been continuous across centuries; however, sometimes that overall narrative has been lost due to the imposition of settler perspectives that constrain Native activism. Recent Native activist movements in the United States and Canada such as the anti-Keystone Pipeline protests and Idle No More received a lot of attention from both the public and the media, but there was an impulse to define these movements within binary categories like “male or female” or “successful or unsuccessful.” Using an Indigenous-centered approach and decolonizing methodology, this thesis examines the American Indian Movement of the 1960s and 1970s, the Keystone protests, and Idle No More. A close examination of the importance of intergenerational change to Native activists and the role of Native women in these movements and eras reveals the dynamic legacy of Native activism that defies categories such as gender and failure or success. Working across and beyond binaries, Native activists since the 1960s have drawn on diverse strategies to create decolonized Indigenous futures.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".