Religion and Realism in International Law: China’s Perspective on the Israeli-Palestinian Conflict
Bibliographic record
Abstract
The purpose of this article is trying to answer the following question: What else a State needs to consider, besides its international legal obligations, when developing its foreign policy? I hope to demonstrate how factors such as religion and realism affect a country’s foreign policy by using the case of Israeli-Palestinian conflict and China’s interactions with the two nations. China successfully adapt realistic attitude in making its middle-east policy. It supported Palestine for the ideology of anti-imperialism, but established relation with Israel for the need of national interest. In this article, I would give a general introduction of the history of China’s relation with Palestine and Israel, including several key incidents that carry great significance in this triangular relationship. Trough my introduction, I hope to demonstrate how religion and realism affects a nation’s foreign policies, as well as international law. I would discuss China’s involvement in the Israeli-Palestinian conflict from the perspective of international relations. First, I will focus on the changes and evolvements of China’s relationships with Palestine and Israel throughout history, and briefly discuss what China has done in the past with the two countries. Second, I will introduce how Chinese society views the Israeli-Palestinian conflict. In the end, proposals will be made for China’s future policy in the 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".