To condone, condemn, or ‘no comment’? Explaining a patron’s reaction to a client’s unilateral provocations
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".