Integrating ibsa s into Nigeria’s Energy Framework: Insights from Canada and Australia
Bibliographic record
Abstract
Abstract Public participation is recognized as essential for equitable and sustainable energy development. However, in Nigeria, existing legal framework for public participation, such as the Environmental Impact Assessment (eia) Act fall short of ensuring meaningful engagement and benefit sharingwith host communities. This article examines the potential of Impact and Benefit Sharing Agreements (ibsa s) as a complementary tool to existing statutes, drawing lessons from jurisdictions like Canada and Australia, where ibsa s have been used to foster Indigenous participation, ensure fair compensation, and enhance environmental protection. Through comparative legal analysis, the article identifies five key conditions necessary for the successful adoption of ibsa s. By evaluating the relevant Nigerian laws, the article highlights both areas of alignment and divergence between the Nigerian legal framework and the ibsa s. The article concludes that while Nigeria has taken steps towards public participation, more legal reforms are needed to enable the integration of ibsa s into the Nigerian jurisprudence, for an even more inclusive decision-making process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".