Bibliographic record
Abstract
This book evolved out of our long-standing interest in Indigenous voices of resistance to colonial paradigms, and how Indigenous people actively challenge the limiting identities imposed upon them by the state and popular media.We ask how Indigenous actors intervene in established institutions through media tactics to disrupt the typical flows of image and discourse found in popular culture.As we discuss, institutions can be negotiated and subverted even when they were not originally designed to serve the best interests of Indigenous people.For Indigenous critics debating the efficacy of established institutions, the stakes are very high.As Indigenous people live the effects of colonization, they are concerned with the ways in which their participation in institutions may inadvertently disadvantage their communities.They are also in a race against time to empower disillusioned young people.According to a Health Canada study published in 2015, the suicide rate of First Nations youth is five to six times higher than that of non-Indigenous youth, with Inuit youth suicide rates among the highest in the world.The legacies of colonial violence continue to plague Indigenous people, and many see both government and privately funded institutions as instrumental in this process, including laws and policies that work to dispossess Indigenous people of their lands and self-determining authority, at times resulting in their displacement and the removal of children from their communities.Too many Indigenous people suffer racism, inadequate government support, disproportionate policing and incarceration, and poverty.They are victims
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.376 | 0.213 |
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".