Benefit Sharing in the Arctic: A Systematic View
Bibliographic record
Abstract
Benefit sharing is a key concept for sustainable development in communities affected by the extractive industry. In the Arctic, where extractive activities have been growing, a comprehensive and systematic understanding of benefit sharing frameworks is especially critical. The goal of this paper is to develop a synthesis and advance the theory of benefit sharing frameworks in the Arctic. Based on previously published research, a review of literature, a desktop analysis of national legislation, as well as by capitalizing on the original case studies, this paper analyzes benefit sharing arrangements and develops the typology of benefit sharing regimes in the Arctic. It also discusses the examples of various regimes in Russia, Alaska, and Canada. Each regime is described by a combination of principles, modes, mechanisms, and scales of benefit sharing. Although not exhaustive or entirely comprehensive, this systematization and proposed typologies appear to be useful for streamlining the analysis and improving understanding of benefit sharing in the extractive sector. The paper has not identified an ideal benefit sharing regime in the Arctic, but revealed the advantages and pitfalls of different existing arrangements. In the future, the best regimes –in respect to sustainable development would support the transition from benefit sharing to benefit co-management.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".