An energy superpower or a super sales pitch? Building the case through an examination of Canadian newspapers coverage of oil sands
Bibliographic record
Abstract
In 2006, Stephen Harper boldly pronounced Canada as an “emerging energy superpower” to a variety of international audiences, including at the G8 meeting in London. While this label is likely more representative of a marketing campaign than reality (Hester, 2007), it is important to understand the degree to which the Canadian media has embraced it. This paper determines the extent to which Canada’s national newspapers, the Globe and Mail and the National Post, and its largest paper, the Toronto Star, adopted the “energy superpower” frame in their reporting about Alberta’s oil sands over a 25-month period. The oil sands were selected as a case study because proponents of Canada as an “energy superpower” cite the development of Alberta’s oil sands as a key component of the country’s new-found status. To discover how this new label was intertwined into the broader discourse on oil sands development, I used content and discourse analysis to examine newspaper stories over 300 words in length that contain “oil sands” or “tar sands” in the lead paragraph and/or headline. While my study found few instances of news stories containing the term, it did find that these newspapers more closely adopted Harper’s underlying ideas about what an energy superpower is than the more activist government traditionally associated with the term
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".