MétaCan
Menu
Back to cohort
Record W3047413082 · doi:10.1177/0964663920946430

Securing the Future: Transformative Justice and Children ‘Born of War’

2020· article· en· W3047413082 on OpenAlexafffund
Erin Baines, Camile Oliveira

Bibliographic record

VenueSocial & Legal Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCriminologyScholarshipEconomic JusticeTransformative learningNeglectSociologyIsolation (microbiology)Conflict resolutionGender studiesPsychologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

Children born as the result of conflict related sexual violence often embody painful memories of war-affected communities. As a result, children ‘born of war’ experience abuse and neglect, social isolation and a sense of never-belonging. Existing scholarship grapples with the challenges of seeking justice for children ‘born of war’ given the complex ways their suffering is entangled with that of so many other victims. In post-conflict northern Uganda, a community-based organization composed of survivors of forced marriage and motherhood collectively seeks justice for their children in a process locally referred to as child tracing. The Women’s Advocacy Network brings together differently affected victim groups to help identify the paternal relatives of their children, mediate conflict and transform fractious relationships in order to secure a future for their children. Through this process, children who once divided communities, propel a collective reach towards justice.

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 score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.021
Scholarly communication0.0070.005
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.321
Teacher spread0.283 · 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

Citations33
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueSocial & Legal StudiesSame topicGender, Security, and ConflictFrench-language works237,207