Reflections and Reinforcements of Public Apathy: Newspaper Coverage and Framing of Missing and Murdered Indigenous Women, Girls, and Two-Spirit People In Canada
Bibliographic record
Abstract
This dissertation discusses the alarming number of missing and murdered Indigenous women, girls, and Two-Spirit people in Canada. The primary focus of this dissertation is to identify and explain patterns in both the quantity of and framing in newspaper stories on this issue. It argues that the amount of newspaper coverage this issue receives and how it is discussed in the public domain has important consequences in attributing the responsibility for the causes of and solutions to this violence. Utilizing 50,154 news articles in 310 online and print national, local, daily, and broadsheet English-language newspapers from 1960-2023, spanning across all regions in Canada, I conduct an extensive qualitative and quantitative media analysis. The objective of this analysis was twofold: 1) assess newspaper coverage over time and across regions to determine if patterns in the coverage change with the political and social environment; 2) analyze the differences in coverage among victims of violence to understand which individuals are considered to be newsworthy and why. This project finds that Indigenous women, girls, and Two-Spirit people are largely absent in news stories, both in terms of how much coverage they receive and in the substance of these stories. In addition, the amount of coverage they receive and how they are framed broadly remains static over time and across regions. Although there are substantial differences in the quantity of coverage they receive depending on their age and if they are missing or murdered, most of the coverage frames this issue in the context of murder trials, police investigations, and the perpetrators of violence. Using these data, I argue that newspaper coverage of this issue reflects and reinforces public apathy towards missing and murdered Indigenous women, girls, and Two-Spirit people, in turn legitimizing inadequate public and political responses to this issue. It is also emblematic of the deep entrenchment of racism and sexism in Canadian news media. The implications of this dissertation are clear: the absence of sustained news media coverage and current framing of this issue is not conducive for the necessary social and political change needed to redress this violence.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".