Bibliographic record
Abstract
The extraction/processing of the Canadian oil sands is predominately a political phenomenon, and less so an economic one. This is evident in three specific ways. First, the demand that is now existent for the oil sands results directly from the historically profligate oil use on the part of the United States (i.e., urban sprawl). (As I show in Chapters 4 and 5, urban sprawl in the United States is itself a political phenomenon.) US oil consumption has played a huge role in the disappearance of so-called easy oil—with America annually consuming 20 to 25 percent of world petroleum production. The International Energy Agency (IEA) in 2010 declared that conventional petroleum production peaked in 2006. 1 With conventional oil extraction seemingly declining, “hard” petroleum, such as the oil sands, oil shale, deepwater petroleum (e.g., in the Gulf of Mexico), and the like, becomes economically feasible. Moreover, continuing massive consumption of gasoline/oil by the United States creates an economic environment favorable to producing oil from the tar sands, and other unconventional petroleum sources (as well as difficult to reach pools of crude—e.g., Arctic Ocean oil 2 ). (Higher production/processing costs for “hard” oil means that the cost of a barrel of petroleum must be above a certain price point [e.g., $50] to be able to bring these energy sources profitably to market. 3 ) These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.251 | 0.099 |
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".