MétaCan
Menu
Back to cohort
Record W2559320005 · doi:10.1093/rsq/hdw015

Justice, Reconciliation, and Ending Displacement: Legal Empowerment and Refugee Engagement in Transitional Processes

2016· article· en· W2559320005 on OpenAlexaboutno aff
Anna Lise Purkey

Bibliographic record

VenueRefugee Survey Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeTransitional justiceDignityEmpowermentInternally displaced personPolitical scienceRepatriationContext (archaeology)Economic JusticeAgency (philosophy)Refugee lawDisplaced personSociologyCriminologyLawGeographySocial science

Abstract

fetched live from OpenAlex

Although there is a growing acknowledgment that the resolution of conflict and the resolution of situations of displacement are inseparable and that both require the engagement of displaced populations, refugees continue to be largely excluded from meaningful participation in peace processes and transitional justice initiatives. Using a dignity-based conceptual framework, this article asserts that it is critical that we recognize refugees as rights-bearing actors capable of exercising agency within the transitional process. To this end, this account explores the important role that meaningful engagement of the refugee communities in transitional justice can play in the process of repatriation and reconciliation. Given the potential benefits of participation in protecting individuals from instrumentalisation and domination by powerful actors, legal empowerment of refugees is proposed here as a potential strategy for ensuring that the needs and interests of refugees and refugee communities are acknowledged and addressed within transitional justice processes and in the context of refugee return.

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)0.010
metaresearch head score (Gemma)0.016
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 score0.014
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.045
Scholarly communication0.0140.014
Open science0.0010.019
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.039
GPT teacher head0.331
Teacher spread0.292 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRefugee Survey QuarterlySame topicAsian Geopolitics and EthnographyFrench-language works237,207