Iran-Saudi Relations: Is Pilgrimage a Mirror of Conflict?
Bibliographic record
Abstract
A series of incidents have recently brought the Persian Gulf to the brink of war. While all the countries in the region are wary of the catastrophic consequences that a war could have, they appear trapped in a highly risky vicious cycle of mutual suspicion, inflammatory rhetoric and tit-for-tat actions. The region is in dire need of a functional regional security system capable of managing risks, facilitating dialogue and enabling peaceful resolution of conflicts while at the same time favouring the emergence of cooperative order to replace confrontation. To address the shortcomings of the Gulf security environment, this ebook aims to answer several questions and to propose a new approach to exit from the current stalemate. What are the key drivers of the current regional insecurity? What options are there to respond to the unfolding crisis and why have previous efforts to create an effective regional security architecture failed? How can a new regional security-building approach emerge? The eBook proposes a security building continuum approach made of gradual, informal and incremental steps and incorporating non-traditional security instruments as a pathway toward achieving long-term sustainable security in the region.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".