Bibliographic record
Abstract
The purpose of this paper is to determine under what circumstances foreign intervention exacerbates sectarian conflict. Since the vast majority of academics do not pay heed to the argument that sectarian conflict is simply the result of ancient hatreds, economic, political, and social factors that result in sectarian conflict must be analyzed. To determine what these factors are and how they interplay with intervention and its associated outcomes, this paper will first review the appropriate literature on foreign intervention and sectarian conflict and then apply relevant theories to three case studies in the Levant covering 1990 to 2014. This paper will utilize the theory that sectarian conflict is produced when groups collectively fear for their future, which eventually provokes a security dilemma and a conflict spiral. It logically follows that any conditions that increase perceptions of fear or exacerbate the security dilemma or conflict spiral are the circumstances under which foreign intervention exacerbates sectarian conflict. Ultimately this paper concludes that high levels of poverty, preexisting civil conflict, the presence of a marginalized sectarian group, and the presence of manipulative leaders in the context of an intervention targeting a state’s government are the circumstances under which intervention exacerbates sectarian conflict.
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 | 0.003 | 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 | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".