Bibliographic record
Abstract
In Canada the sharing of the government (public) revenues generated from natural resource extraction or use with Indigenous communities is evolving in different formats - some provinces approach it through modern treaties, others through non-treaty, policy-based agreements and some do not have a resource revenue sharing mechanism in place. In addition to government arrangements, companies have been proactive in sharing economic benefits with local and Indigenous communities through signing an impact and benefit agreements (IBAs) or other agreements (e.g. community participation agreements) over the last decade, some of which also include financial provisions to share revenue with Indigenous communities. Both practices have been studied well, but with limited data published on the actual implementation. Using the case of British Columbia, this research attempts to add some insight into it using the reports published online under Extractive Sector Transparency Measures Act and First Nations Financial Transparency Act. The study conducted semi-structured interview with government and industry representatives to further understand the challenges associated with both practices. Overall, the mining share constituted for less than ten percent in select First Nations annual budgets, but together with other natural resource revenue from forestry, clean energy and natural gas sectors under the province’s revenue-sharing agreements with First Nations it could serve as a major source of funding for these communities. The challenges identified within the industry through semi-structured interviews pointed to the lack of clarity and guidance from government iii on the engagement and consultation processes with Indigenous Peoples affected by their operations, which may significantly delay the projects or lead to undesired outcomes. Given the focus on social issues from investors within the changing landscape of investment practices that incorporate environmental, social and governance (ESG) factors in the investment decision-making, the research also looked into investors’ perspective on what their expectations are for mining companies on community engagement and benefit-sharing. The study revealed that a growing trend of responsible investing has a potential to impact the performance of mining companies to ensure that local and Indigenous communities have an opportunity to engage in resource development planning and receive a fair share of benefits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| 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".