Biden Cancels Keystone XL: How Alberta’s Local Media Is Framing the News
Bibliographic record
Abstract
Climate change mitigation is frequently presented as a binary trade-off between economic development and environmental protection, particularly in Canadian news. The economic value and “necessity” of the oil sands is often used as a justification for perpetual extractivism and further expansion of energy infrastructure. News media have an important role in delivering information and shaping public opinion on large-scale issues in society, including the issue of climate change. This Major Research Paper explores Alberta’s local news media’s reaction to the cancellation of the Keystone XL pipeline by examining the narratives used in their reporting of the news. I highlight the importance of studying Canadian local news influence on public perception of issues, as this is often not adequately represented in environmental communications research. I conducted an exploratory content analysis of articles from three local news publications, the Calgary Herald, the Red Deer Advocate, and the Lethbridge Herald. The findings suggest economic arguments that justify oil sands expansion largely overshadow pro-environment representation of the issue. This study also uncovers high use of victim narratives that perpetuate extractivism by using “us versus them” populist rhetoric in storylines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".