Representation in the Era of Reconciliation: Media Framing of Indigenous Politics in Canada
Bibliographic record
Abstract
In the last six years, reconciliation has been highlighted (at least rhetorically) as one of the key political goals of the Canadian state as it claims to re-orientate and navigate its relationship with Indigenous Peoples. Despite being widely embraced across settler society, state-sanctioned reconciliation remains a fundamentally contested concept that has been critiqued for its failure to create space for the revitalization of Indigenous sovereignty and self-determination. This dissertation utilizes a comparative case study and mixed-methods approach to explore how print-based news media – a key source of learning about Indigenous issues for members of settler society – have contributed to the discursive construction of Indigenous-settler reconciliation in Canada. Using the techniques of frame and content analysis, the dissertation examines 533 articles and editorials published across three empirical cases – the Trans Mountain Pipeline Expansion, the 2020 Wet’suwet’en Land Defense and the Indigenous Languages Act. The key research finding is that the Canadian news media continue to produce systemic patterns in coverage which reject, marginalize and erase the territorial rights and claims of Indigenous Peoples. By extension, these patterns in coverage contribute to the construction of a ‘Doctrine of Discovery’ model of reconciliation defined by increasing socioeconomic and cultural equality for Indigenous Peoples, while at the same time reaffirming the exclusive territorial sovereignty of the Canadian settler state. The dissertation concludes that rather than helping to move the relationship between Indigenous Peoples and settlers forward along a path of reconciliation, power-sharing, and the resurgence of land-based self-determination, the news media are continuing to construct discourses and representations that work against the political objectives of Indigenous Peoples and reinforce settler colonial power relationships in Canada. In offering this conclusion, the study highlights the role that non-Indigenous authorship and sourcing play in shaping news coverage of Indigenous issues in Canada. The dissertation makes original contributions to both the Indigenous politics and political communications literature by explaining how the news media are contributing to the ongoing politics of Indigenous-settler reconciliation in Canada.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".