“The Forgotten People:” Analyzing the Invisible, Intersectional Discrimination Against Métis Women
Bibliographic record
Abstract
The Métis is a group of indigenous peoples in Canada.Having experienced centuries of injustices, beginning with colonialism dating back to the 16 th century, culminating with military defeats in the 1800s and the establishment of residential schools, and continuing with structural injustices in the 21 st century, Métis people have long been, and continue to be marginalized and made invisible in the Canadian society.In particular, Métis women born between 1997 and 2012 face intersectional discrimination based on not only race, but also a multitude of identity factors, including gender, age, geographical location, health, sexual orientation, and lateral violence from First Nations peoples.This paper uncovers the multilayered oppression against young Métis women through a literature review and uses several theories to analyze the invisibility of this discrimination in society, including color-blind racism, collective shame, lack of understanding of intersectionality, and Mauvaise foi (bad faith).To address the invisible, intersectional discrimination against young Métis women, several suggestions and possibilities could be considered.These include amending the education system, fostering group affiliation, bringing structural changes to federal policies and funding system, and cooperating with other indigenous nations such as First Nations and Inuit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".