From Durable Solutions to Holistic Solutions: Prevention of Displacement in Africa
Bibliographic record
Abstract
Abstract The problem of internally displaced persons (IDPs) is prevalent in Africa due to conflicts, development projects, man-made as well as natural disasters that are commonplace on the continent. The Kampala Convention, which is an innovative tool, seeks to provide African States with a roadmap towards long-term solutions to prevent and eradicate the causes of displacement. As such, African States, humanitarian agencies, the African Union and other stakeholders should go beyond durable solutions, which are reactive, and pay more attention to proactive strategies to eradicate the factors that lead to displacement. To do this, there is need to adopt a holistic approach to address the question of IDPs on the continent through a three-tiered strategy that includes; firstly, short-term strategy to protect the IDPs by providing safety and security, freedom of movement as well as basic livelihood to the IDPs; secondly, medium-term strategy to restore IDPs’ dignity and ensure adequate living conditions through return, resettlement, (re)integration, reparation, restitution and rehabilitation; and thirdly, long-term strategy to eradicate root causes of displacement and fostering an environment conducive to respect of human rights, rule of law and good governance.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".