How professionals respond to disruptive effects of artificial intelligence on their jurisdiction: The role of interactive governance
Bibliographic record
Abstract
The increasing adoption of artificial intelligence (AI) in professional work has disrupted established jurisdiction, frequently eliciting defensive responses from professionals. However, limited research has systematically examined how professionals respond to such disruptions. Based on 86 interviews, 240 hours of non-participatory observation, and 20 documents collected over 47 months of fieldwork in Chinese public hospitals, this article investigates the Intelligent Prescribing Review (IPR) system - an AI tool designed to assist physicians with prescribing and dispensing - in order to analyze professionals' responses to AI disruption. The study identifies four models of interactive governance employed by professionals: intra-professional division, inter-professional coordination, professional-AI collaboration, and professional-organization consultation. These responses are shown to be shaped by the interplay of the institutional environment, organizational strain, and a relationship-oriented society. By presenting interactive governance as the central mechanism, the analysis moves beyond dichotomous narratives that depict professional responses as mere acceptance or resistance. The findings highlight an important shift in professional jurisdiction, from reliance on individual knowledge-based expertise toward interactive governance through collaborative negotiation.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".