Analyzing control, capacities, and benefits in Indigenous natural resource partnerships in Canada
Bibliographic record
Abstract
Our work analyzed Indigenous partnership arrangements and conditions associated with natural resource development, specifically, the capacities identified by Indigenous peoples needed to participate in resource wealth generation. The review was needed to take stock of previously understudied and new partnerships emerging in Canada’s rapidly growing natural resource sectors where cross-cultural collaboration is becoming a feature, and in some cases a requirement, of new ventures. Results illustrate nine categories of arrangements (i.e., land use/regional planning processes; IBAs; MOUs; Indigenous businesses, joint ventures; environmental assessments; revenue sharing; advisory committees; and regional economic councils) used by Indigenous communities and their partners to assert their control and derive benefits from natural resource extraction. These included highly formal and technical legal arrangements, such as Impact and Benefit Agreements, and less formal arrangements such as Memorandums of Understandings and advisory committees. Using the five capitals’ (social, human, financial, built, and natural) approach we also synthesized existing knowledge of partnership capacities and benefits. We found benefits in each of the five capital areas, most of which were forms of human capital. Employment (50%), improved decision making (46%), and also financial support (33%) were the top cited benefits. Results build to the conclusion that differences exist between capacities needed to start working together (pre-existing supporting conditions), and those built through collaboration (new or enhanced capitals as beneficial outcomes). Development models will produce more and sustainable benefits where capacity building is both an explicit process objective and outcome of new partnership designs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".