General Resource Revenue Sharing between the Government of Alberta and Indigenous Peoples in Alberta: Policy Options, Implications, and Considerations
Bibliographic record
Abstract
For any jurisdiction, having the option, some would say opportunity, to share significant resource wealth is in many ways, “a good problem to have”. Significant comparative research has been conducted with specific reference to the wide variety of models in Canada and internationally that enable Indigenous communities to benefit from resource development through revenue sharing agreements. This Capstone is a response to the lack of a jurisdictionally-focussed analysis for Alberta, where the concept is not currently applied but where the debate continues. The goal of this Capstone is to equip policymakers with both context and data needed to ensure a constructive and informed dialogue around the important and complex issue of sharing Alberta's general resource revenues with indigenous communities in the province. It explores the historical and legal context of the issue, as well as the context of Indigenous Peoples in Alberta. Its Literature Review covers the sources of Alberta's significant but volatile resource revenues, the vastly different financial capacities of Alberta's indigenous communities, the current distribution of Alberta's resource revenues, and the positions of relevant parties. The Analysis component evaluates models for revenue sharing from across Canada and around the world, as well as estimating the potential costs of revenue sharing if those models were applied to the Alberta context. These indicate an annual cost to the Government of Alberta of anywhere from $66 million to $2.5 billion per year by 2019-20. Finally, it provides an overview of several key considerations for policymakers. The Capstone's primary recommendation is the development of an inclusive and politically arms-length process involving all relevant parties to study various policy options and make recommendations. This process would be focussed not only on the “how” of revenue sharing, but also on the “why”. This would allow the Government of Alberta and the province's indigenous communities to better ensure that the ultimate outcome will best reflect their available resources and preferences, and build a stronger and more durable foundation for future cooperation.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".