Fueling divisions: a multimodal analysis of Canadian petro-nationalism in the social media discourse of “oil sands strong”
Bibliographic record
Abstract
This research conducts a multimodal critical discourse analysis of social media posts by Oil Sands Strong (OSS), examining their role in advocating for resource extraction through the use of “petro-nationalism” discourse. The analysis uncovers three key aspects: First, the posts employ multimodal semiotic resources to create dichotomies and blend nationalist and xenophobic discourses with energy discourse. Second, they integrate diverse multimodal symbolic elements from various discursive frameworks to forge a collective identity for the local Canadian community. Lastly, the posts stigmatize foreign oil producers, new energy industries, and environmentalists, employing multimodal symbols to obfuscate the argument's focal point and generate a hybrid discourse. Through visual symbols, assertive text, and argumentation of topoi, the posts present an exaggerated and distorted image of petro-nationalism. The study concludes that these multimodal posts aim to reshape Canadian energy politics, promote biased sentiments, exploit cultural prejudices, and formulate petro-nationalism narratives for resource extraction advocacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".