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Record W3024594433 · doi:10.1177/0020702020917167

Peacekeeping works: The UN can help end civil wars

2020· article· en· W3024594433 on OpenAlexaff
A. Walter Dorn, Robin Collins

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsCanadian Forces College
Fundersnot available
KeywordsPeacekeepingCriticismPolitical scienceLawSociologyPolitical economy

Abstract

fetched live from OpenAlex

Despite some harsh criticisms of United Nations (UN) peace operations, research demonstrates that many UN missions are successful, though evaluations depend on how success is defined. Even UN missions that fail in one or more aspects provide a net benefit to peace processes and help to save lives and alleviate human suffering. While an understanding of the flaws and limitations of peace operations can help improve the operations, some unfair criticism must be directly challenged. For instance, contrary to critiques in a recent paper by Séverine Autesserre, the UN has helped end civil wars, and it does not have a fixation on elections nor does it ignore the bottom-up approach. Most UN multidimensional missions pursue multiple levels of engagement, from local to national leaders. Elections are a key way to engage locals. With decades of experience, the UN has many positive lessons to offer in making, keeping, and building peace.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.312
Teacher spread0.296 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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