The use and abuse of forced migration and displacement as a weapon of war
Bibliographic record
Abstract
It is undeniable that forced migration and displacement is a “weapon of war.” To understand the ever-escalating refugee crisis in the world today and how it might be addressed, it is necessary to examine its “root cause(s).” Organized political violence in the form of either war or protracted armed conflict or oppressive dictatorial authoritarianism or totalitarianism are the prime drivers of forced migration and displacement in the world today. The most recent statistics on the world's forcibly displaced demonstrates their direct correlation to war and protracted armed conflict. The vast bulk of the world's forcibly displaced come from war torn countries. What is too often ignored in the academic literature on refugees and forced migration is that civilian non-combatants are often used as “weapons of war.” Mary Kaldor's “new wars” thesis is premised on the notion that wars and protracted armed conflicts defy solution because it is in the interest of the opposing combatants to continue the fighting, consequently, wars are characterized as “endless.” This has obvious implications for the continuous increase in the number of forced migrants in the world today. And, further, the “new wars” thesis postulates that civilian non-combatants are deliberately targeted during protracted armed conflicts or wars. Kelly Greenhill has presented evidence that “coercive engineered migration” is utilized to a State's advantage over another State. The weaponization of civilian non-combatants is a trigger for mass forced migration. Three recent cases dealing with genocide are offered as illustrations: Bosnia; Rwanda; and the Syria Civil War, with the Sieges at Homs and Aleppo. The article concludes with the consideration of the “endless cycle of war” that includes forced migration, smuggling, human trafficking, and other international crimes such as dealing in drugs and arms. All these elements are inter-connected and feed off each other and to be able to break this “endless cycle of war,” there must be a concerted and simultaneous effort to address all these things together. Only by doing so can we ever hope to achieve a “sustainable peace” and thereby end the use and abuse of forced migration and displacement as a “weapon of war.”
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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 | 0.004 | 0.007 |
| 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 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".