Introduction: reframing narratives of peace-building and state-building in Africa
Bibliographic record
Abstract
This paper introduces the Special Issue that grew out of a research project at the African Leadership Centre, which was supported by the Canadian International Centre for Development Research (IDRC). Like the underpinning research, the papers in this volume engage with two aspects of the state-building and peace-building debate and foreground the theory of “conversation” as a useful lens through which to advance the pursuit of sustainable peace in Africa. First, we challenge the dominant approach that constructs liberal state-building as an essential condition for durable peace in societies emerging from armed conflict. Second, we examine the extent to which various forms of political settlements are able to deliver sustainable peace and as a result, more peaceful and viable states. The concept of “conversation” is the thread that connects the two elements of the research. The notion of conversation reverses the conventional view of the relationship between peacebuilding and state-building while re-centring a particular dimension of political settlement. We argue that peacebuilding should be conceived as part of the conversations occurring along the state-building continuum in the affected societies. This shifts the traditional approach of privileging the technical over the political, power over agency, and the international over the national and local. This paper introduces the articles in this volume, which include conceptual and empirical case-studies and it discusses implications for policy and practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".