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Record W2559146526 · doi:10.5038/1911-9933.11.1.1455

MediCapt in the Democratic Republic of the Congo: The Design, Development, and Deployment of Mobile Technology to Document Forensic Evidence of Sexual Violence

2017· article· en· W2559146526 on OpenAlexvenueno aff
Karen Naimer, Widney Brown, Ranit Mishori

Bibliographic record

VenueGenocide Studies and Prevention · 2017
Typearticle
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsnot available
FundersU.S. Department of State
KeywordsHuman rightsDemocracyContext (archaeology)Software deploymentSexual violencePolitical scienceField (mathematics)Internet privacySociologyPublic relationsLawEngineeringComputer sciencePolitics

Abstract

fetched live from OpenAlex

This review essay provides an overview of the MediCapt app and the steps Physicians for Human Rights has taken to design, develop, and field-test the app in the Democratic Republic of the Congo. It also explores advocacy opportunities that the app’s emerging technology may facilitate down the road. This review essay also identifies the many challenges and questions that we have grappled with and lessons learned as we seek to deploy MediCapt in a low-resourced and politically unstable context and take it to scale beyond DRC. Finally, in sharing the details of this case study, we hope to emphasize both the promise of new mobile technology for human rights organizations as well as the significant legal and ethical responsibilities that accompany it.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.359
Teacher spread0.291 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations10
Published2017
Admission routes1
Has abstractyes

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