MétaCan
Menu
Back to cohort
Record W3183947983 · doi:10.1111/1758-5899.12993

The End of the Liberal World Order and the Future of UN Peace Operations: Lessons Learned

2021· article· en· W3183947983 on OpenAlexafffund
Katelyn Cassin, Benjamin Zyla

Bibliographic record

VenueGlobal Policy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPeacekeepingHegemonyDirectivePolitical economyGlobal governancePolitical scienceOrder (exchange)DemocracyGlobalizationPublic administrationSociologyLawEconomicsPolitics

Abstract

fetched live from OpenAlex

Abstract Global conflicts are becoming increasingly transnational and often involve non‐state actors. These trends mirror the diffusion of power that has resulted from globalization and the erosion of liberal hegemony since the late 1990s and early 2000s. Though projections vary among scholars, the future international system is likely to involve a diversification of actors exerting influence in all policy spheres, including conflict‐response and peace operations. The United Nations (UN) and other liberal actors, historically dominant in peace operations, must adapt to remain relevant in a future where their governance of operations, and the underlying liberal democratic goals on which they are based, can no longer be assumed. In light of this waning liberal international order, this paper examines the core lessons learned from the past 70 years of UN peace operations to infer what future UN peacekeeping might look like and what adaptations will be necessary for this new environment. In so doing, we prepare recommendations for a truly localized and contextualized approach to peace operations that is expansive, representative and non‐directive, ultimately necessitating the UN and other liberal actors to adopt higher risk tolerance and relinquish exclusive control over conflict‐response and 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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.357
Teacher spread0.338 · 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 designTheoretical or conceptual
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

Citations11
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueGlobal PolicySame topicPeacebuilding and International SecurityFrench-language works237,207