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Record W2981027858 · doi:10.1177/0022343319875202

To condone, condemn, or ‘no comment’? Explaining a patron’s reaction to a client’s unilateral provocations

2019· article· en· W2981027858 on OpenAlex
Jeehye Kim, Jiyoung Ko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Peace Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRivalryChinaOrder (exchange)State (computer science)Political sciencePower (physics)BusinessLawPolitical economySociologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract What explains a patron’s decision to publicly condone, condemn, or forgo commenting on its client’s unilateral provocations? We present a new theoretical framework that identifies a patron’s two strategic considerations – maximizing its sphere of influence and avoiding entanglement – and factors that affect them. We claim that whenever a patron faces a great power rivalry or a vulnerable client, it is more likely to condone its client’s provocations in order to safeguard its sphere of influence. On the other hand, when the risk of escalation looms large, the patron is more likely to condemn its client’s provocations in order to avoid entanglement. Focusing on the Sino-North Korean patron–client relationship, we test our theory on an original dataset that tracks China’s official reactions to provocations initiated by North Korea. We find that China tends to condone North Korea’s provocations when the USA criticizes them, and refrains from condemning when North Korea is domestically fragile. We also find that China is more likely to condemn its client’s provocations in the period after North Korea became a nuclear state. In addition, we draw on examples from the USA–Pakistan and the USA–Israel patron–client relationships to illustrate our causal logic. This article offers new insights on how a patron manages its client’s unruly behavior, and provides the first large-N evidence on China’s responses to North Korean provocations from 1981 to 2016.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.125
GPT teacher head0.475
Teacher spread0.350 · 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