Community Driven Development in Contexts of Conflict. Concept Paper Commissioned by ESSD, World Bank
Bibliographic record
Abstract
Violent conflict represents not only a significant barrie r to\ndevelopment; it also wipes out efforts to improve the situation.\nExperience from many developing countries has shown that\nCommunity Driven Development (CDD) programmes have\nbeen particularly effective in establishing or expanding\nessential social services and physical infrastructure at the local\nlevel. However, using CDD approaches in a conflict context as\na means in post-war rehabilitation represents new challenges.\nWhen carried out in contexts of past or persistent conflict,\nCDD projects are confronted with some major challenges:\n•communities where projects are set may be deeply\ndivided;\n•power is unequally distributed;\n•lines between combatants and civilians may be\nblurred;\n•a need to address past traumas may give rise to calls\nfor inquiries or trials; and\n•economic recovery and basic services may be urgently\nneeded.\nNonetheless, the point of departure in this paper commissioned\nby the CDD unit of the Environmentally and Socially\nSustainable Development (ESSD) Network of the World Bank,\nis that participatory and demand-led development approaches\nmight potentially address three critical concerns in conflict\ncontexts:\n•The need for speedy and cost-effective delivery of\nreconstruction assistance.\n•The need to improve the state-citizen relationship.\n•The need to create alternative forms of community\norganisation that foster reconciliation between\nfactions of the society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".