CHARTING THE COURSE FOR A BLUE ECONOMY IN NIGERIA: A LEGAL AGENDA
Bibliographic record
Abstract
Ocean and coast based economic activities are increasingly being recognized as key drivers for supporting global economies. This move towards a “blue economy” is becoming widespread in view of the paucity of land resources being experienced globally by promoting sustainable and inclusive economic growth using oceanic resources. The sustainability of these ocean-based activities must however be intricately linked with the existence of a comprehensive and cohesive legal framework to align marine conservation with the extractive and exploitative endeavors. This paper analyses the potential for a blue economy in Nigeria and examines the adequacy of the existing legal regimes on marine environmental protection meant to reduce the risks of intensified ocean-based activities resulting into unsustainable environmental impacts. The paper submits that deriving sustainable wealth from ocean-based activities in Nigeria is achievable given the existing legal framework for marine environmental protection in the country. It however recommends the need to further tighten the noose around the implementation protocols of these laws to better integrate the health of the ocean ecosystem into the development of the country’s ocean resources.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".