Indigenous women and girls’ rejection of settler statecraft representation and our collective reclamation of narrative in mainstream media
Bibliographic record
Abstract
This thesis argues that Canadian settler colonial statecraft and its hegemonic politics of recognition create the circumstances for Indigenous women and girls to be negatively represented in mainstream media. Mainstream media, especially news and journalism, have the power to pull audiences toward or push them away from a humanizing representation of Indigenous women and girls. Mainstream news media is a settler institution that reifies and upholds settler statecraft and the settler colonial project. I propose that combining the politics of recognition with self-recognition is a potential solution and path forward for Indigenous women and girls toward narrative reclamation. Though outright rejection of the politics of recognition may seem necessary, I argue that it is impossible to escape without it first coalescing with self-recognition, as we cannot enact our futurisms if we are not surviving. I provide context for the contemporary negative representation of Indigenous women and girls through an exploration of the colonial roots of systematic disempowerment associated with our representation as the ‘squaw.’ This negative representation perpetuates stereotypes that ultimately result in higher rates of violence against us and settler apathy toward these injustices. To exemplify the reality and gravity of negative representations against us, this thesis includes a case study into the mainstream news media representation of the life and case of Tina Fontaine, which analyzes three Winnipeg news outlets from 2014-2019. The final chapter delves further into how the politics of recognition and self-recognition can work together for narrative reclamation and collective Indigenous re-empowerment.
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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 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".