Stuck in the tar? The implications of Canadian mainstream news media representations of a just [energy] transition from Alberta's bitumen sands
Bibliographic record
Abstract
Alberta’s Tar Sands are one of the most environmentally and socially destructive fossil fuel projects on Earth. Socio-political resistance or acceptance will determine the Tar Sands’ future: whether production continues or whether it is phased out to ensure meaningful climate action in Canada. I examined how news media communicates a “Just Transition” from the Tar Sands and proposed that their written formulations have wider social implications. My analysis revealed that news media framed the Tar Sands as being part of Canada’s future energy landscape with a techno-corporate-managerial role in the energy ‘transition’. Additionally, news media failed to discuss the Just Transition in a meaningful way. The implications are climate misconceptions and Just Transition illiteracy, foreclosing the opportunity to inform the public on transformative solutions, which could risk public acceptance of climate action. My findings raise questions on the news media’s role going forward and broader issues of climate communication.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".