Power Transition Theory and the Middle East: Can We Predict a Conflict?
Bibliographic record
Abstract
This paper will attempt to analyze the current regional rivalry between Saudi Arabia and Iran by applying Power Transition Theory tweaked to be implemented in a regional manner. This paper will be divided into four sections, with some inter-lap in the middle sections. The first section will focus on a literature review of Power Transition Theory and the previous theories of International Relations that preceded it. This will include an analysis of the third image of war historically and under Kenneth Waltz and for Power Transition theory will focus on the works of Organshi, Lemke, Krugler and other contemporary academics. The second section of this paper will look at the historical context that shapes the contemporary issues of not only Saudi Arabia-Iran relations but as well as Sunni-Shia relations and Middle Eastern politics at large. The third section will focus primarily on the contemporary issues between the two countries including but not limited to; ethnoreligious divisions, regional hot conflicts and instability (as well as Iran and Saudi Arabia’s involvement in them), and international oil markets. Lastly, this paper will apply Power Transition Theory to see whether or not there a burgeoning conflict between Saudi Arabia and Iran is on the horizon or not. This paper will provide not only a predicative exercise of a specific conflict but also a contemporary check on a celebrated conflict theory. Discipline: Political Science Honours Faculty Mentor: Dr. Jean-Christophe Boucher
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".