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Record W3185535889 · doi:10.1080/17502977.2021.1955501

Storytelling: Restorative Approaches to Post-2003 Iraq Peacebuilding

2021· article· en· W3185535889 on OpenAlexaff
Ruba Ali Al-Hassani

Bibliographic record

VenueJournal of Intervention and Statebuilding · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsYork University
Fundersnot available
KeywordsPeacebuildingStorytellingRestorative justiceSociologyPolitical scienceSocial constructionismConflict resolutionNarrativeCriminologySocial sciencePolitical economy

Abstract

fetched live from OpenAlex

Top-down national reconciliation initiatives overlook the significance of, and connection between, story and culture in social conflict resolution. They have thus failed to emerge in post-2003 Iraq. This article draws on the theoretical insights from social constructionism to examine the potential of centreing storytelling within a bottom-up restorative peacebuilding process. It posits that integrating storytelling-based practices, such as restorative justice dialogue and restorative education, within Iraq's legal and educations systems can promote an inclusive, cross-communal public discourse and facilitate bottom-up and inclusive peacebuilding practices.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.010
Scholarly communication0.0060.004
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.137
GPT teacher head0.350
Teacher spread0.213 · 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 designQualitative
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

Citations11
Published2021
Admission routes1
Has abstractyes

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