Bibliographic record
Abstract
This chapter is a critique and expository of colonialism, epistemology, pedagogy, and praxis of higher education and its role in concealing the horror our children and Peoples and Nations have endured for over a century and counting. Genocide-informed awareness is necessary to understand the impact of the crime of genocide – forcibly transferring Indigenous Peoples’ children into residential institutions and causing the collective serious bodily and mental destruction. The post-secondary education system is complicit in masking the reality of the Canadian state’s intent to destroy and the devastation of the crime on Indigenous Peoples and Nations. Genocide-informed awareness is needed to understand how words oppress and maintain domination and dehumanization. The forcible transfers nearly wiped out our cultural, spiritual, and national identities as human groups, not individuals, and the effects of this colonial destruction continue to mount intergenerationally with no end in sight. More importantly, Indigenous Peoples require this understanding so that we heal. We are not subparts of this colonial state; we are national groups to be protected by the United Nations Convention on the Prevention and Punishment of the Crime of Genocide. Another note is that I write as a nehiyaw/cree iskwew/woman in the first person. Genocide is an act of colonial violence against my people and Indigenous Peoples across our Great Turtle Island.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".