Authoritarian decision making at the interface of the state, science and the public : politics of biodiversity conservation and biosafety regulations in China
Bibliographic record
Abstract
Science and technology are an inherent part of political decision making in modern times. How do decision makers balance legitimacy, power and knowledge? Existing literature on the issue only focuses on liberal democracies and neglects authoritarian regimes in both theoretic and empirical investigations. In particular, it cannot answer how authoritarian regimes respond to challenges in governance, particularly ones rising from technically complex and uncertain policy fields such as biodiversity conservation and climate change. My research addresses this issue by investigating how scientifically complex international environmental norms are filtered through the systems of expert consultation and public contestation in an authoritarian political system. Drawing on 150 semi-structured interviews conducted between 2015-2019, my dissertation examines the policy processes in China’s nature conservation and biosafety regulation, and seeks to explain how the authoritarian state significantly strengthened biodiversity conservation in these two issue areas while the developmental and vested interests were stacked against them. Building on Jurgen Habermas’ three normative models, I first propose a typology of authoritarian policy decision-making at the science-politics interface, including authoritarian decisionist, technocratic, and public contested models. While all three models are present in China’s biodiversity governance, a “state-corporatist technocracy” model stands out as a more routine type of consultative decision making that often boils down to a bureaucratic-scientist alliance against environmental norms. I argue that two factors—the political salience and knowledge-based collective actors—are key to overcome this problem for the successful diffusion of environmental norms. In particular, I find that an emerging domestic epistemic community in protected areas and a knowledge-intense proxy civil society at the state-society nexus in biosafety regulation play critical roles in the norm contestation. Using a modified Multiple Stream Framework in the former and drawing on social movement theories in the latter, I identify how strategic trade concerns and changes in the party’s leadership raised the political salience, enabling the collective idea agency to shape policy.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".