Opportunities for First Nation Prosperity Through Oil and Gas Development
Bibliographic record
Abstract
Over the next decade, more than 600 major resource projects worth approximately $650 billion are planned for Canada. First Nations communities have a unique opportunity to benefit from these developments: every oil and gas project currently proposed in western Canada implicates at least one First Nations community, giving them an opportunity to increase employment and economic prosperity through collaboration in energy development.The First Nations communities that will be affected by oil and gas development have a young, unemployed population that can serve as a labour force for the proposed projects. The First Nations are one of the youngest and fastest-growing groups in the country. Whereas the median age for non-aboriginal Canadians was 41 years in 2011, the median age for the First Nations was 26 years. Moreover, the median age for First Nations communities that are in a position to benefit from the proposed oil and gas development ranges from 22 to 30 years. While the national unemployment rate is 7.1 percent, the unemployment rate for First Nations reserves is a staggering 23 percent. Unemployment rates are particularly high (20 to over 42 percent) in First Nations communities that are located in areas identified for oil and gas development.The unique combination of population density in remote, resource-rich areas, a growing and young population, and a high level of unemployment places the First Nations in a unique position to benefit from energy development in Canada.While there are some obstacles to overcome so that the First Nations can benefit from oil and gas development, solutions can be derived from successful examples of collaboration between governments, First Nations, and industry members. One such example is the partnership between the Haisla Nation and Chevron Apache, which brought a $350-million liquid natural gas project in British Columbia to fruition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".