How Institutions Communicate Change: Casuistry and Loosely Coupled Change in China’s Market Transformation
Bibliographic record
Abstract
How do institutions think about change? Building on Mary Douglas’s famous contention that institutions think by means of analogy, we suggest that institutions think about change by means of irony. Irony is pronounced during times of profound change when the rhetoric and the reality of change can be inconsistent. We show that the Chinese Communist Party (CCP) has enacted what we term loosely coupled change—change in which symbolic meanings and material practices are only weakly connected and retain their independence. The CCP employed the rhetorical form of irony, known as casuistry, to legitimize a change to market systems as being incremental while in practice radically adopting market systems and dismantling socialist practices. We contribute to research on institutional messaging by examining the hermeneutic depth of casuistry. We also contribute to research on organizational change by explicating how casuistry reconciles contradictory ideologies and facilitates loosely coupled change.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".