Indigenous Peoples in Canadian TV News. A Corpus-based Analysis of Mainstream and Indigenous News Discourses
Bibliographic record
Abstract
In contemporary Canada, what non-Indigenous people know (or assume) about Indigenous peoples comes from TV news. For years, the media have played a significant role in circulating biased representations of Indigenous peoples, acquired from colonial discursive constructions of the ‘Indian’. At the same time, in Canada’s varied mediascape, master narratives and counter-narratives interfere with and interrupt each other. The struggle to gain that power to ‘represent’ is constant and leads to ongoing conflict between those who are being illegitimately objectified on TV and those who want to represent them. In light of this, by focusing on the genre of newscasts, this book attempts to identify and uncover the linguistic phenomena that characterize different stances in the representation and self-representation of Indigenous peoples in contemporary Canadian TV news discourse, from a Critical Discourse perspective, using Corpus Linguistics tools. The study proposes an analysis of mainstream and Indigenous news stories, aired in the years 2012-2013 during the so-called #idlenomore national revolution, by three widely accessible news providers: the Canadian Broadcasting Corporation (CBC), CTV Television Network and APTN, the Aboriginal Peoples Television Network. While CTV and CBC are identified as carriers of dominant discourses and representations, APTN is not merely a network broadcasting Indigenous stories, but it is made for and by Indigenous peoples. The newscasts, arranged into two specifically designed corpora, are examined in order to spot linguistic preferences, and state whether an alternative, ‘postindian’ form of news discourse is possible, what viewpoints it reflects and how it aligns or dis-aligns with hegemonic discourses on the ‘Indian’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".