Public diplomacy and Ethio-Egyptian relation; enhancing mutual understanding on the Grand Ethiopian Renaissance Dam
Bibliographic record
Abstract
GERD has been a source of tension and a diplomatic deadlock between Egypt and Ethiopia. Diplomacy and cooperation are the only means to resolve these issues. The inability to recognize the current reality that emanates from contradictory narration leads Ethiopia and Egypt relations on the GERD into full mistrust and misconception, and even moving toward confrontation. Accordingly, this study was intended to examine how public diplomacy enhances mutual understanding between Ethiopia and Egypt on the GERD. This study employed a qualitative research approach with in-depth literature review. The findings of the research reveal that securitization, counter-securitization narration, and over-politicization are dominant in the GERD at the cost of the objective reality that muddies Nile water utilization. Hence, the study identified strong popular narratives based on ultra-nationalism and political rhetoric on the GERD that were built by successive government officials, and media, scholars based on past and current events deepened the mistrust. The researcher recommends that Ethiopia and Egypt should widen diplomatic efforts in addition to conventional diplomacy by implementing public diplomacy elements to understand their vice-versa genuine concern and build confidence in GERD, which ultimately fosters effective cooperation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".