Winnipeg Sun newspaper and Canadian Indigenous people: a qualitative media analysis
Bibliographic record
Abstract
ABSTRACT This research study used a qualitative analysis to explore how the Winnipeg Sun represents Indigenous peoples. The study was guided by two research questions included: 1) how the Winnipeg Sun represents the Indigenous population; and 2) do the Winnipeg Sun articles about Canadian Indigenous people promote intolerance to Indigenous Peoples in Winnipeg. The purpose of the study was to examine news articles printed in the Winnipeg Sun between 2015 and 2020, evaluate how Indigenous people were represented to determine if the discourse accurately portrays Indigenous people. This exploratory study arose from a news magazine article in McLean’s (MacDonald, 2015) that stated that Winnipeg is the most racist city in Canada. Being Indigenous or as I call myself Anishnabe, this statement impacted me personally and I could not help but wonder how the Winnipeg Sun affects the way society considers Canadian Indigenous Peoples. Intolerance of Indigenous People is visible and openly discussed today through the printed media and other media, such as Facebook and twitter. These negative misrepresentations reflect printed media from earlier eras. The way mainstream society looks at and talks about Indigenous people reflects their contempt. This research study examined whether negative print in the Winnipeg Sun presents a damaging view of Indigenous people. One question it sought to answer is has what is printed in the Winnipeg Sun been influenced and reinforced through a biased approach by the authors of the articles in the newspaper. A historical viewpoint on this study affected me on a personal level. Being Indigenous or Anishnabe I cannot help but wonder how the Winnipeg Sun affects the way society considers Canadian Indigenous people. The history behind the desire to do a thesis on this topic arose from my personal experience which started back in 1968. Back in 1968 I personally experienced intolerance because of race. At that time intolerance toward the Indigenous people was evident but not discussed in public as it is today. The school I went to at that time had a grade one teacher who belittled me daily for being Indigenous. She would point out the way I was dressed and my accent. I felt ashamed but could not tell anyone because I felt I was doing something wrong even though I was not. Almost daily a viral recording of racist actions or rants toward Indigenous people is shown and questions emerge within me why people criticize and ridicule Indigenous people. The article by Brian Giesbrecht titled Indigenous Start Blocking the road is an example of how we are being mocked by the Winnipeg Sun, his comment to the Indigenous population…you will not find it by dancing around a highway…which shows his ignorance because dancing for the Indigenous people is a ceremony.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".