When socialization fails: breaking the habit of engagement with China
Bibliographic record
Abstract
Abstract Why do states sustain failing policy? The literature on path dependency and cognitive habits shows how foreign policy logics become axiomatic. Yet these explanations focus too much on people and not enough on policies. We argue that the content of policies matters for the degree and depth of their entrenchment. Policies with long-term time horizons, immeasurable objectives and diffuse effects are especially vulnerable to stasis. Engagement policies toward China are a paradigmatic example. We focus on Canada's engagement policy, which exhibits both stasis and change, and refer to similar policies of other countries. Drawing on primary evidence including interviews with high-level diplomats and decision-makers, we find that engagement aimed at socializing China was sustained despite growing evidence of its failure—including through a multi-year diplomatic crisis. Change only became possible through ‘institutionalized debate’, meaning the purposeful creation of formal channels for debate. The article makes three contributions. First, we identify a novel explanation of stasis, showing that the content of policies matters. Second, we introduce a practical pathway—institutionalized debate—that can disrupt stasis once a policy logic is habituated. Finally, we identify an aspect of socialization ignored in the literature: when operationalized as policy, it becomes resistant to reversal.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".