The Study of Effects of Iraq's New Political Geography on the National Security of the Islamic Republic of Iran Based on Lee Norji Martin’s Theory
Bibliographic record
Abstract
Given that the geography of modern times was one of the most important factors affecting the relations between Iran and Iraq, in this article we have tried to examine the influence of Iraq's new political geography variable factors on the national security of the Islamic Republic of Iran. For this purpose, regardless of the security implications of Iraq's geopolitical constant factors, with placing three variable factors in the political geography means population, natural resources and socio-political institutions in the form of five security variables for Lee Norji. Martin, meaning the political legitimacy, civil rights and ethnic and minorities, military strength, the strength of economic management and natural resources, fifteen areas will be formed (in the annexes, these fifteen areas are in the table). The question that arises here is that what is the impact of Iraq's new political geography on the national security of Iran? The hypothesis that we are looking to review it is that changes in some areas of geopolitical of Iraq, after the United States invaded Iraq, made threats to the national security of Iran. The main objective of this paper is to study Iranian security changes in the first ten areas and to present solutions. Since the Iraq has not achieved stability yet, next five areas need further research and in this article do not occur.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".