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Record W4389496347 · doi:10.1016/j.cosust.2023.101392

A resilience-based transformations approach to peacebuilding and transformative justice

2023· article· en· W4389496347 on OpenAlexaff
Per Olsson, Michele‐Lee Moore

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTransformative learningPeacebuildingEconomic JusticeTransitional justicePsychological resilienceResilience (materials science)Political scienceConflict transformationState (computer science)Process (computing)Phase (matter)SociologyEconomic systemLawPsychologySocial psychologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

Moving from a state of war or violent conflict will require a transformation, but there are no guarantees that transformations automatically lead to peace, sustainability, and justice. This review focuses on the temporary phase when a system is in limbo between the existing, dominant state and a new alternative state. We combine insights from a resilience approach to transformations with peacebuilding and transformative justice studies to focus on three roles that hybrid approaches to transformative and transitional justice may play in this phase, including 1) addressing ‘backlash’ dynamics, 2) strengthening the capacities needed to navigate cross-scale dynamics of conflict, and 3) responding to additional shocks, crises, and disturbances beyond the primary conflicts. Together, these findings advance the theoretical foundations for understanding peacebuilding as a transformative change process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.022
Scholarly communication0.0050.007
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.037
GPT teacher head0.359
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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