Towards Understanding the Cameroon-Nigeria and the Eswatini-South African Border Dispute through the Prism of the Principle of uti possidetis juris Customary International Law
Bibliographic record
Abstract
This article examines the Cameroon-Nigeria and Eswatini-South Africa border disputes from a comparative perspective within the framework of the doctrine of uti possidetis juris in customary international law. Extant scholarly works on these two border disputes have not been sufficiently cogent to enable an evaluation of the relevance and shortcomings of uti possidetis juris. The study methodology is qualitative and includes archival and newspaper sources, in-depth interviews and focus group discussions. This study reveals that the strict application of the uti possidetis juris doctrine to the Cameroon-Nigeria dispute over Bakassi was inappropriate and did not generate the anticipated peace and security. The Eswatini-South Africa bilateral talks, aimed at adjusting colonially inherited borders, were an attempt to comply with uti possidetis juris, but flopped. Following the Cameroon example, the Eswatini monarchy then contemplated taking South Africa to the International Court of Justice (ICJ). But the two scenarios were different, and the invocation of uti possidetis juris was not an appropriate instrument for resolving the Eswatini- South Africa border dispute. Eswatini irredentism has persisted because of the country’s commitment to Sobhuza’s testament, which sanctioned the unity of the Eswatini people.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".