Violent Conflicts as an impediment to the Achievement of Millennium Development Goals in Africa.
Bibliographic record
Abstract
Abstract This paper aims to stimulate a debate on how Violent Conflict (VC) is obstructing the success of Millennium Development Goals (MDGs). It briefly examines progress with the MDGs in Africa using officially published United Nation (UN) Reports and global MDG monitoring information. It also provides readers with a preliminary exposition on how violent conflicts pose the greatest challenges to progress with achieving the MDGs. It argues that violent conflict makes chronic poverty even worse – from household to national levels. The paper warns that many countries in Africa will fall far behind in attaining the MDGs by the targeted date of 2015 unless African states and regional institutions such as the African Union can put a decisive end to the current conflicts and address the threat of new conflicts. Having presented comparative evidence from various countries (those on track to meet the MDGs and those lagging behind), the significance of conflict prevention, conflict resolution and peace building in increasing the likelihood of Africa’s achieving the MDGs within the timeframe were highlighted. Highlighting the critical importance of strengthening the link between durable peace and sustainable development, it was concluded that the MDGs, as a framework for policy, programs and international partnerships to reduce poverty, must explicitly articulate how to end violent conflict and support war-torn countries (and those emerging from conflict) as a matter of priority and that they must receive special consideration. Key words: Millennium Development Goals, Violent Conflict, Poverty, Regional Institutions, Conflict Resolution, Peace Building, Sustainable Development, International Partnerships.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".