Indigenous communities sustainable development framework for LNG developments in Northwest B.C.
Bibliographic record
Abstract
The extractives sector has the obligation to contribute to sustainable development in areas where resource exploitation occurs. Fulfilling this expectation is challenging in resource-dependent towns, that are periodically exposed to boom-bust dynamics. In northwest British Columbia, several large Liquefied Natural Gas (LNG) terminal projects have been proposed, involving high capital costs and several thousand workers for the construction phase. Indigenous Peoples are often negatively affected by such large developments, as their culture and sustenance is tied to the land and water. Many of these peoples are also unable to benefit from such developments, due to a lack of support mechanisms and the necessary training or education required for good paying jobs. This study investigates how large resource developments can contribute to sustainability in B.C. First Nations communities by finding ways to enhance benefits and minimize impacts from boom-bust dynamics. Two socio-economic surveys were conducted with the Kitsumkalum First Nation, which is one of the Tsimshian Tribes potentially affected by LNG developments. Additionally, 31 interviews were conducted with LNG, mining, government, economic development and First Nations representatives, from which common themes were identified and ranked. Results showed that although high school graduation rates (16% to 34%), university education rates (4.5% to 7.3%), and unemployment rates, (29.2% to 17.2%) have improved for on-reserve Kitsumkalum members between 2006 and 2016, many continue to struggle economically. Education, training and employment (ETE) was collectively ranked by all interviewed sectors as the most important for First Nations to move towards a sustainable future, while all sectors individually ranked ETE as No. 1, except for First Nations, who ranked the removal of social barriers as No. 1 and ETE as No. 2. The need for good governance to roadmap effective changes was ranked No. 2, while the need to remove social barriers was ranked No. 3 by all sectors. In light of these results, a new framework was proposed, which incorporates the need for community characterization, a strategic sustainable development plan, good governance, and improved shared decision making and partnerships, in order to better facilitate sustainable development of Indigenous communities within the context of large-scale resource developments.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".