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Record W7084683185

La protection des civils dans les opérations de paix des Nations Unies

2024· dissertation· en· W7084683185 on OpenAlexaboutno aff

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
FundersUnited Nations
KeywordsObligationPoliticsPeacekeepingInternational peaceInternational relationsQuarter (Canadian coin)Security councilCold war
DOInot available

Abstract

fetched live from OpenAlex

A quarter of a century (1999-2024) after the express consideration of “protection of civilians” in the mandates of peace operations, United Nations multidimensional peace missions are struggling to convince of their capacity to effectively protect civilians in the countries where they are deployed. However, over the last decade, significant progress has been made on the legal, conceptual and operational levels. This thesis therefore attempts to answer the fundamental question of effectiveness, but mostly that of the efficiency of the mandates for the protection of civilians in United Nations peace operations.This thesis demonstrates that the protection of civilians in operations can be improved if it benefits from a firm and disinterested political commitment from States. Furthermore, because it is above all a positive obligation that must be implemented with due diligence, the protection of civilians in United Nations peace operations cannot prosper without asking all stakeholders, at all levels, to report and take responsibility for their failure to fulfill their obligations of protection, whether of a primary or secondary nature. In front of the security challenges of this century and the next, the alignment between the statements of national and international institutions and their actions will depend on the implementation of these measures.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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