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Record W4229551551 · doi:10.5334/29

Searching for Conflict Related Missing Persons in Timor-Leste: Technical, Political and Cultural Considerations

2013· article· en· W4229551551 on OpenAlexvenueno aff
Naomi Kinsella, Soren Blau

Bibliographic record

VenueStability International Journal of Security and Development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsnot available
Fundersnot available
KeywordsFontStyle (visual arts)Timor lesteContext (archaeology)Independence (probability theory)Computer scienceLinguisticsSociologyMathematicsHistoryArtificial intelligenceStatisticsLiteratureArtPhilosophyEthnology

Abstract

fetched live from OpenAlex

This paper outlines the context in which many thousands of people went missing in Timor-Leste between 1975 and 1999. The issues related to estimating the exact number of missing are discussed, followed by a review of the mechanisms implemented by the government and civil society since independence to attempt to examine and investigate the fate of missing persons. The paper then examines the technical details involved with searching for the missing which impact on the effectiveness of the different mechanisms. Further complexities related to scientific and religious/cultural beliefs when dealing with the missing are discussed. The paper concludes with questioning the to date ad hoc approach to the search for the missing in Timor-Leste, and providing suggestions for ways that the future search for the missing can realistically continue in light of other competing development priorities.

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)0.009
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0060.002
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.066
GPT teacher head0.375
Teacher spread0.309 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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