“MUCH OF WHAT WE LEARN ABOUT INDIANS, WE LEARN AS CHILDREN”: COUNTER-IMAGES TO BIASED AND DISTORTED PERCEPTIONS OF FIRST NATIONS IN NATIVE CANADIAN JUVENILE LITERATURE
Bibliographic record
Abstract
In Canada, many spelling and arithmetic books, as well as other simple books for school beginners perpetuate numerous falsehoods about Native peoples, which need to be eradicated. First Nations are not heathen savages always carrying tomahawks, bows and arrows, nor are they objects to be counted along with apples and balls. Scrutinising reading materials is crucial to ensure that books accurately reflect Native values and world views, instead of promoting ignorant myths that contribute to biased perceptions of Others and even of ourselves. It is with this in mind that, in my paper, I intend to reflect upon a children’s picture book written by Thomas King, because it represents conscious efforts on the part of its author to correct the above-illustrated stereotypes, thus providing Native and non-Native peoples with authentic and clearer representations of the First Nations of Canada.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.033 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".