Resilience, Adaptive Peacebuilding and Transitional Justice
Bibliographic record
Abstract
There exists a vast body of scholarship, written from multiple disciplinary and cross-disciplinary perspectives, exploring the complexities of resilience. It is striking, however, that resilience has received only limited attention in the context of communities and societies that have experienced conflict, violence and large-scale human rights abuses. It has similarly attracted little attention within the field of transitional justice. The book’s introduction sets out how and why this unique volume, which includes eight case studies, seeks to address these gaps. It proceeds to outline and discuss the three central strands that run through the book and weave the different chapters together, namely resilience (which the book approaches as a systemic and social ecological concept), transitional justice and de Coning’s adaptive peacebuilding. What this edited volume ultimately seeks to demonstrate is that thinking about resilience as a multi-systemic concept opens up a space for developing new ways of theorizing and operationalizing transitional justice that are more responsive to the wider social ecologies that link individuals and communities to their environments – and to the broader systems within which transitional justice work takes place. Responsiveness to these social ecologies and systems, in turn, is a crucial part of adaptive peacebuilding.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".