Language, Diplomacy and Global Governance: Discourse Strategies in a Middle Eastern UN Address
Bibliographic record
Abstract
The article applies Critical Discourse Analysis (CDA) to King Abdullah II’s 2024 speech at the United Nations General Assembly, uncovering how he strategically employs humanitarian, legal, and geopolitical discourses to shape international perceptions of a conflict. Unlike previous research on Western political rhetoric, this study highlights how a Middle Eastern leader navigates global governance structures to construct political legitimacy, critique power asymmetries, and advocate for human rights. The findings reveal that King Abdullah integrates emotive language, statistical evidence, and legal references to frame the conflict as a global moral and legal crisis, moving beyond regional narratives. Additionally, the study identifies how historical intertextuality, including references to his father’s past UN addresses, strengthens Jordan’s diplomatic credibility and positions it as a principled advocate for peace. The analysis also highlights how the speech strikes a balance between critique and diplomacy, avoiding direct vilification while effectively critiquing the failures of international institutions. By framing the United Nations as ineffective in enforcing justice, King Abdullah aligns Jordan’s discourse with broader post-colonial resistance narratives, challenging dominance in global governance. This study contributes to CDA and political discourse analysis by offering a new framework for understanding how smaller states use language to challenge hegemonic structures and influence international diplomacy.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".