Bibliographic record
Abstract
I struggle mamere To bring Your words Into nokum's Cabin But the words Are in battle Competing for my mind I am a mixed-blood woman raised in Canada where my two ancestries have competing worldviews, from social, political, and religious ideology to ancient philosophies. These mixed ancestries also come with different social expectations. In the social-political world of Native Studies where I walk daily, my French grandmother, mamere, is argued as coming from a world of privilege because she was white-skinned, and my Cree grandmother, nokum is thought to come from a world of oppression because she was dark-skinned. Yet both my grandmothers experienced abuse and prejudice. How and where the abuses originated may be different, but they did occur. I have a lot to learn from my grandmothers, but it has taken me many years of inner conflict, self-righteousness, and pain to get to this understanding. To acknowledge both grandmothers having been oppressed means I cannot continue to think of the world in simplistic, binary terms of colonizer/colonized. I must legitimize the equality of suffering in both cultures. Indeed, my worldviews had been turned upside down as I began to identify with the feminist movement, nonetheless it is nokum's world that was shattered, demeaned, and distorted, so it is her world I bring to you today with this story. Another day I may talk about my mamere's patriarchal world, but today is for nokum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".