Governance for reconstruction in Africa: challenges for policy communities and coalitions
Bibliographic record
Abstract
This article seeks to advance analyses and responses to conflict prevention and reconstruction in Africa that go beyond state‐centric perspectives to include a range of non‐state players. Drawing on examples from both Uganda and Canada, it focuses on the activities of NGOs that have ‘partnered’ with state‐based actors in various peacekeeping and peace‐building operations as well as on the increasingly important role played by think‐tanks. The latter have emerged in Africa as major contributors to the proliferating literature on the political economy of violence, an approach that recognizes that African conflict reflects imperatives of production and consumption in relations that juxtapose Africa’s political institutions and cultures with international and global political economies. The article argues that novel forms of ‘security communities’ are emerging from the non‐state/state/international partnerships and coalitions that have developed around contemporary issues like ‘blood’ diamonds, small arms, debt and HIV/AIDS, thus drawing attention to connections between conflict and development.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.010 | 0.009 |
| 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".