INUIT GIRLS MAKE MEDIA: RESISTING STEREOTYPES THROUGH PARTICIPATORY ACTION RESEARCH
Bibliographic record
Abstract
Historically, entertainment media have reproduced inaccurate and stereotypical media representations of Indigenous peoples. In this thesis, I draw on concepts such as Stuart Hall’s theory of media influence, Pierre Bourdieu’s concept of symbolic violence, George Gerbner and Gaye Tuchman’s ideas of symbolic annihilation in order to analyze how media representations of Indigenous women and girls perpetuate stereotypes, and how alternative media productions might counter them. Using ethnographic and participatory action research (PAR) methodologies, I then explore these issues using empirical material. First, I conduct an Ethnographic Content Analysis (ECA) to reveal how Indigenous women and girls are represented in music videos, identifying patterns along themes of beauty standards, stereotypes, and power and agency. Second, I explore how Inuit girls self-represented when given access to resources. To do this, I collaborated with local Indigenous organizations in Rankin Inlet, Nunavut, to facilitate a three-day music video camp for Inuit girls. A year later, following PAR principles, I involved the girls in the data analysis process; themes in the girls’ videos included friendship, connection to nature, Inuit culture and the importance of positive representation. Overall, this thesis provided an opportunity for raising awareness among the Inuit girls that by making their own media, they have the power to create their own self-representations and resist stereotypes. In this way, girl-led self-representations have the potential to change lives and communities.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".