Bibliographic record
Abstract
‘The Republic of Bangui’ or ‘the Republic of Monrovia’ are phrases we sometimes hear from practitioners to describe post conflict countries where very few services exist outside the capital city. This is especially the case for security – the critical public good in post conflict countries. In response to the need to bring security services closer to the citizens who often need them most, the Government of Liberia and the United Nations are piloting a new approach financed by the UN Peacebuilding Fund (PBF) – the so-called ‘Justice and Security Hubs’. The donor community and the United Nations are watching closely. If this works, there is indication from UN officials that the model could potentially be replicated in other settings such as the East of the Democratic Republic of the Congo (DRC), Haiti and the northern states of South Sudan. If the hub concept is capable of being adapted and successful elsewhere, the United Nations will not only have added a new instrument to its peacekeeping toolkit but will also firmly demonstrate how the UN Peacebuilding Fund can in essence be catalytic in fostering long-term and comprehensive approaches to peacebuilding. This practice note outlines the process of developing and constructing the first hub in Liberia, which is due to be partly operational by the end of 2012, and provides a prognosis on its chances for success.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".