A fair distribution of oil and gas revenues for Newfoundland and Labrador: a feasibility study
Bibliographic record
Abstract
This study aimed to investigate a way to a sustainable future of Newfoundland and Labrador through the introduction of a Sovereign Wealth Fund (SWF) using the province’s oil and gas resources. The theoretical frameworks for this study are the capital approach of weak sustainability, environmental justice, and resource curse. With these frameworks, a comparative case study analysis has been adopted to investigate cases of two jurisdictions that are already successfully operating SWFs funded by oil and gas revenue to build more sustainable societies by sustaining their economic, environmental, human, and social capitals. Based on the case studies, this feasibility study examined the following questions: 1) What impacts did the Norwegian SWF have on the sustainability of Norway? 2) What impacts did the Alaskan SWF have on the sustainability of Alaska? 3) How does the oil and gas industry affect Newfoundland and Labrador's sustainability and what improvements should be made? 4) Will Newfoundland and Labrador be able to ensure sustainability with their oil and gas revenue? The study concludes that introducing a SWF could help to ensure the sustainability of Newfoundland and Labrador, with several supporting policies, such as diversified funding sources, building a framework that can benefit local people, and achieving social consensus.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".