Bibliographic record
Abstract
The Alberta government is heavily exposed to energy price volatility as it relies to a great extent on revenue derived from the production of oil and natural gas. Energy prices change substantially and unpredictably, causing large and uncertain movements in revenues. Adjusting to these movements typically involves economic, social and political costs. Alberta government revenues are considerably more volatile than the revenues of other provinces, but Alberta’s own-source revenues less royalty payments are of similar size and volatility as those of other provinces. Several methods to reduce the volatility of revenues are assessed. An often-suggested method, tax base diversification (for example, use of a retail sales tax), is shown to have a minor effect on overall revenue volatility since Alberta’s royalty revenues are such a large share of total own-source revenues. Revenue smoothing using futures and options markets can be expensive, is associated with significant political risks, and cannot eliminate all revenue volatility. The Canadian dollar tends to appreciate (depreciate) when energy prices rise (fall), so exchange rate movements have smoothed Alberta government revenues, although not by a large amount. A simulation using Alberta data shows that a revenue savings fund could significantly reduce revenue volatility. This type of fund leads to greater revenue stability because the revenue it contributes to the budget in any particular year is based on revenues averaged over prior years. Revenue uncertainty is also reduced with a savings fund since future revenue depends on known past contributions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".