Reforming Alberta's Heritage Fund: Lessons from Alaska and Norway
Bibliographic record
Abstract
The governments of Alberta, Alaska, and Norway have all created funds in which to deposit some of the revenues they receive from non-renewable natural resource activities. Despite Alberta’s rich natural resource endowments, its Alberta Heritage Savings Trust Fund is smaller than the others because of its relative underfunding and because of chronic withdrawals of most income from the fund. This paper explores the history and structure of the three funds, and offers recommendations for reform in Alberta, including a formal rule for the contribution percentage and institutional mechanisms to encourage proper fund management.The paper finds that if the Alberta government had consistently deposited 25 percent of its non-renewable resource revenues from 1982-2011 — as the Alaskan constitution requires — total contributions would have been $42.4 billion, rather than the actual contributions of $9.1 billion during this period. And if the Alberta government had followed Norway’s example, and contributed 100 percent of its non-renewable resource revenues into its Heritage Fund, then from 1982-2011 total contributions would have been $169.5 billion, rather than $9.1 billion.In order to fulfill its mission of preserving Alberta’s rich resource wealth for future generations, the government should seriously study the lessons from Alaska and Norway laid out in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".