Bibliographic record
Abstract
The province of Alberta, home of the Canadian oil sands, contains the third largest known oil reserves on the globe. Extraction of bitumen from the oil sands is a key reason for the growth of Alberta’s economy. A major public debate within the province is whether Albertans can realize even greater value from this resource by increasing the amount of bitumen processed in the province via upgrading and refining. This report investigates the major arguments in the debate over whether or not more domestic bitumen processing would benefit the province, with the goal of determining whether or not the government should be involved. It finds the arguments in favour of increasing the amount of local upgrading are based largely in philosophical arguments that jobs and government revenue will increase, without offering a proven economic basis to back the argument. Arguments cautioning against investments into increasing local upgrading and refining capacity point to the current and future market and economic conditions that are causing great uncertainty about the ability to gain a return on this type of investment. A thorough examination of stakeholder positions, industry actions, and case studies of bitumen upgrading and refining projects in Alberta and British Columbia suggests there is no apparent reason increasing bitumen processing within provincial borders will make Albertans better off. In addition, current government of Alberta policy on this file is spending government revenue and placing taxpayers at further financial risk. Based on my analysis, this report offers alternative policy options for the provincial government around bitumen processing in Alberta.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.448 | 0.265 |
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".