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Record W2924001828 · doi:10.1177/0020702019834724

Middle power liberal internationalism and mediation in messy places: The Canadian dilemma

2019· article· en· W2924001828 on OpenAlexaffabout
Peter Jones

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsImpartialityMediationDiplomacyDilemmaPoliticsPolitical economySociologyForeign policyPolitical scienceConciliationLawInternationalism (politics)Power (physics)Law and economicsEpistemology

Abstract

fetched live from OpenAlex

Canada seeks to increase its role in mediation as part of a renewed liberal internationalist foreign policy. This means confronting the question of how to manage the domestic political consequences of engaging as a mediator with those violating cherished international norms, while also upholding the view that they should be punished. Key to all this is the concept of impartiality, particularly as it relates to the objectives of liberal internationalist countries. This paper explores multiple meanings of the term “impartial” as they pertain to mediation, particularly with respect to the question of mediations involving those who have violated international norms. The paper then explores whether increased support for “arms length” mediations, such as Track Two diplomacy, might allow for more involvement in mediation, while avoiding direct involvement in morally fraught situations. The paper concludes that Track Two can be useful in developing a national capacity for international mediation, and that work can also be done to make Track Two—which is currently based largely on Western concepts—more indigenous. However, support for Track Two does not answer the fundamental question of how Canada, as such, can be more active as a mediator if it is not willing to engage with actors who have committed atrocities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.007
GPT teacher head0.283
Teacher spread0.276 · 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 designObservational
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

Citations5
Published2019
Admission routes2
Has abstractyes

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