Handling the Iraqi Popular Mobilisation Forces In The Post-Islamic State Iraq
Bibliographic record
Abstract

 
 
 This article examines the future of the Iraqi Popular Mobilisation Forces the Islamic State of Iraq and Syria (ISIS) in Iraq. The PMF is militias that assisted the coalition forces in liberating Iraqi cities from ISIS control. However, in the aftermath of that operation, the PMF now poses a major threat to the future of state-building (PMF) after the fall of an umbrella organization of armed in Iraq. Their armed strength and loyalties to leaders other than the Iraqi government, combined with evidence from existing research on pro-regime militias, suggest that the PMF poses a high- threat in an unstable environment if not managed carefully. Therefore, this article addresses the following question: What can be done with the various militias of the PMF to ensure a secure and sustainable future in Iraq? The Iraqi government has already taken the first steps to mitigate the PMF’s threat by integrating them into the national army, but further integration is required. Since undergoing any disarming, demobilizing and reintegrating (DDR) program is unlikely in Iraq at present, this article recommends employing the PMF for the purpose of infrastructure reconstruction or its support.
 
 
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".