Development of an Overarching Transboundary Geospatial Framework: Selected Regimes in Perspective
Bibliographic record
Abstract
In the maritime industry, a significant number of emerging and complex challenges occurring, has engaged limited attention and tenuous governance structures that can hardly deal with such complexities. Hence, the need for practitioners to seek innovative planning approaches for addressing such complexities, particularly the cumulative impacts on the marine environment. Such swelling effect, cannot be overemphasized in the Gulf of Guinea marine region which lies between Cape Lopez in Gabon and Cape Palmas in Liberia. This region, has a long history of conflicting marine uses and unresolved disputes. Prior to 2020, three adjacent nations in this region have engaged in a host of sovereign disputes and domestic conflicts as chronicled in Chapter One. Pressing challenges of this nature, necessitated an extensive interrogation of contemporary literature to help conceptualize and develop an overarching transboundary geospatial framework, capable of addressing such long-standing hurdles in the region. The proposed framework which was developed in light of the research analysis and empirical lessons, sought to address spatial development challenges pertaining to stakeholder engagement, joint agreements, political and legal support, financial, investment and fund management, planning complexities, data acquisition and plan implementation. Essentially, this analytical framework is postulated to maximize regional cooperation and promote peaceful co-existence, enhance coordination and prevent future overlapping entitlements, and protect and develop the environment to accelerate blue economic growth in the GoG marine region
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".