白猫，黑猫还是好猫？“北京共识” 作为政策思维的另类哲学 (White Cat, Black Cat or Good Cat? The Beijing Consensus as an Alternative Philosophy for Policy Deliberation)
Bibliographic record
Abstract
The English version of this paper can be found at https://ssrn.com/abstract=2496090. Chinese Abstract: “北京共识”是一场用超实用观点进行政策审议的哲学运动。和给政策制定者解决问题或原教旨主义者坚持特定经济传统提供政策配方集的发展模式不同，“北京共识”本身承认，每个发展情境中都有一组独特和/或试验性的解决方法，需要将当下的政治、经济和社会环境考虑进去。这种超实用主义需要政策制定者进行更大规模的政策试验，并具有更大的风险弹性。此外，该哲学通过总结近些年中国政策试验中的实践和教训，恰当了表达了其本身的含义。不过讽刺的是，这种哲学也给分析 “北京共识”和中国发展模式之间的差异带来了潜在困惑。本文概述了这种区分，并进一步理论化了以“北京共识”的意向性为支撑，运用超实用主义观进行政策审议的潜在后果。 English Abstract: The Beijing Consensus represents a philosophical movement towards an ultra-pragmatic view of conducting policy deliberation. Contrary to models of development which provide a subset of policy prescriptions for the policymakers’ disposal or a fundamentalist adherence to a particular economic tradition, the Beijing Consensus inherently recognises that each development scenario has a potential set of challenges that may require unique and/or experimental solutions factoring the current political, economic and social environments. This ultra-pragmatism will require the policymaker to engage in greater policy experimentation, and to have a larger risk-elasticity. Further, this philosophy is most aptly demonstrated by looking at the aggregation of practices and lessons learned using the recent policy experiences of China. Ironically, this leads to a potential confusion regarding the analytical distinction between the Beijing Consensus and the Chinese model of development. This article outlines this distinction, and further theorises the potential consequences of employing an ultra-pragmatic view of policy deliberation espoused by the intentionality of the Beijing Consensus.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".