A Critical Discourse Analysis of the U.S.- Canada Tariffs’ News Narrative: Affiliation or Patriotism?
Bibliographic record
Abstract
This study critically analyzes the discursive representations of the 2025 U.S.-Canada tariffs crisis in two American news outlets, namely: MSNBC and Fox News. It aims to investigate three reports from each outlet to shed on how their narratives represents the 2025 tariffs crisis with Canada. That is, it attempts to figure out whether the narrative of each outlet favors national loyalty or the contrasting ideological affiliation. In order to achieve its aim, the research relies on Fairclough’s (1995) Three-Dimensional Approach for the qualitative analysis, and Halliday’s (1994) Transitivity Framework for the quantitative analysis in examining the selected data. The results suggest that the narrative of each channel shows a clear division. That is, MSNBC adopts a critical narrative toward economic and experts’ analyses to undermine the act, and Fox News stresses national security and sovereignty, largely echoing the narrative of the state. This divide contributes to the framing of media as mostly partisan, wherein ideological affiliation overrules unified patriotic stances.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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 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".