The Examination of News Media Representation of Indigenous Murder Victims in Canada: A Case Study of Colten Boushie’s Death
Bibliographic record
Abstract
The power of media outlets such as newspaper and televised news coverage could shape public perception and influence our policies on issues addressed in the news. More specifically, the media representations of Indigenous people in Canada often include racism, stereotypical assumptions, power struggles, and inaccurate accounts of the event being captured (Johnson, 2011). As a result, the western dominant perspective of Indigenous people would not be challenged resulting in the public perceiving Indigenous people as a group to be overlooked upon. To date, existing research on the media representations of Indigenous murder victims in Canada has focused solely on missing and murdered Indigenous women and there is limited knowledge on Indigenous murdered men (Innes, 2015). My thesis addressed the gap in research through a critical race theory-informed case study analysis of media representations of Colten Boushie. Using newspapers and televised news coverage specifically examining the first two weeks of media coverage, this thesis uncovers two competing narratives: 1) news coverage constructed Colten Boushie as an ‘ideal victim’ and 2) news media supported shooter, Gerald Stanley. Additionally, my analysis found that some media coverage used a thematic framing approach to address racism in the province and nationally. These analyses led to several key findings in my research: the first was the sympathetic portrayal of Colten Boushie as an ‘ideal victim’ as it challenges the common media representation of Indigenous people and victims. Secondly, the difference in the amount of newspaper and televised news coverage Colten Boushie received as certain televised news segments varied in their detail of reporting compared to newspapers sampled. Thirdly, selective media outlets ‘dehumanized’ Boushie through iii narratives defending Stanley’s actions as self-defence. Lastly, the media addressed the ongoing experiences of racism faced by Indigenous people in the province. In this current study my research challenged the common stereotypes portrayed in the media regarding Indigenous people in Canada by analysing underlying assumptions. Most importantly, my study extended research in understanding how Colten Boushie was framed in news media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".