Lessons from the street: Using street art to disrupt misrepresentations and invisibility of Indigenous women and girls in Canadian mass media
Bibliographic record
Abstract
This article discusses the potential of street art to counter misrepresentations of Indigenous women and girls in Canadian mass media, where common tropes of being incompetent mothers or criminals, amongst others, are pervasive. In this article, I look at examples of street art that showcase Indigenous women as caring, empowered, and knowledgeable individuals. These examples of street art generate not only alternative narratives on Indigenous mothering, agency, and knowledge but also provide visibility, as research shows the stories of Indigenous women and girls are not consistently seen in Canadian mass media. Negative representations of Indigenous women and girls have been connected to the violence they experience in Canada; therefore, disrupting these misrepresentations and stereotypes is of vital importance.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".