MétaCan
Menu
Back to cohort
Record W1537582462 · doi:10.26522/vp.v3i1.514

Les Nations Unies font-elles plus de mal que de bien ? Les abus sexuels dans la République Démocratique du Congo

2006· article· fr· W1537582462 on OpenAlexaffvenue
Tracy Russell

Bibliographic record

VenueVoix Plurielles · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Une organisation est aussi forte que l’est chacun de ses membres, et si la francophonie se veut une force viable dans l’avenir, il faut qu’elle tienne compte de ces conflits armés qui détruisent plusieurs de ses pays membres, mais aussi qu’elle aide à les résoudre. Toute la population en subit les conséquences néfastes, mais ce sont les femmes et les enfants qui sont les victimes principales de ces conflits. Trop souvent, les violations des droits de la personne commises contre eux sont impunies. Cet article porte sur un tel exemple de conflit, a savoir celui que connaît actuellement le Congo, où des abus sexuels sont perpétrés sur une grande échelle, et dont les auteurs sont, entre autres, les militaires et les civils qui travaillent pour les Nations Unies.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0050.005
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueVoix PluriellesSame topicGender, Security, and ConflictFrench-language works237,207