Regional Resource Benefit Agreements: Knowledge Gaps and Considerations for the Adoption of a Resource Benefit Agreement in Northwest British Columbia
Bibliographic record
Abstract
Major resource projects in Northwest British Columbia have the potential to boost incomes and reshape the local economy (NDIT 2020). However, resource projects often impose large infrastructure and service costs on nearby communities (Franks et al. 2010). Some of the largest stresses on local infrastructure and services occur during the construction phase of the project (Rolfe et al. 2007; Shandro et al. 2014; Ryser et al. 2020). It has been estimated that resource development in Northwest British Columbia has resulted in infrastructure needs surpassing $1.3 Billion (NBCRBA 2019). Recently, local governments in Northwest British Columbia have formed an association to secure the funding required to mitigate the negative impacts from major resource projects. The Northwest British Columbia Resource Benefits Alliance (“NBCRBA”) is currently in negotiations with the Government of British Columbia on a Resource Benefit Agreement (“RBA”) framework intended to allocate a share of future resource revenues, whether royalties, taxes, or corporate revenues, toward filling current and future infrastructure and service gaps in Northwest British Columbia (NBCRBA 2017). Directing some of the benefits of resource projects to local communities through a RBA could be crucial for maintaining social license for further major projects in Northwest British Columbia (Markey and Heisler 2010). This paper examines what an appropriate RBA framework could be for Northwest British Columbia.
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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 | 0.034 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".