Report of the International Conference on the Impact of Armed Conflict and Terrorism on Children and Youth
Bibliographic record
Abstract
Since the Nairobi Conference on the Impact of Armed Conflict and Terrorism on Children and Youth in February 2016, violent conflicts, including violent extremism continue to affect children, youth and their families unabated.The aerial bombings and the use of explosives in war situations as witnessed in Aleppo in 2016 left many children dead and others injured.The brutal military offensive activities against opposition forces left many children killed and huge populations displaced in a significant number of countries.In Somalia, for example, record has it that there was 50% increase in the number of recorded violations against children, while some 615 children were killed in Syria.Thus, armed conflicts and terrorism unfortunately increased during this period and subjected children to untold sufferings.According to available reports, the year 2016 seemed to be the worst year for children since the World War II.The bombings of Aleppo in 2016, in the global attempts to get rid of ISIL, left many people in the world with doubts on the strategies being used to fight wars, where according to International Humanitarian Laws, civilians and public utilities, such as, hospitals and schools are supposed to be protected.But, with modern forms of communication (the Media), it is apparent that the implementation of such laws are truly wishful thinking for those who formulated them after World War II in 1945, as it is a challenge to implement them.Thus, it is currently impossible to avoid bombing public places and utilities in search of the enemy or opposition forces.Similarly, the terrorists or the violent extremists in their efforts to make a point aim at maximum destruction and end up killing many children, while maiming and destabilizing many people.Annex II.List of participants:-No.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".