Bibliographic record
Abstract
Prologue Beijing Consensus was a term initially coined by Joshua Cooper Ramo in 2004, as a superior, and distinctly Asian, developmental model. Ramo's claim of a “Beijing Consensus” triggered much academic interest and resistance in the West, as some questioned whether the developmental policies Ramo's model prescribed accurately described China's path to economic development. In 2007, Randall Peerenboom, in a book titled China Modernizes: Threat to the West or Model for the Rest? , advanced what he termed an “East Asian Model,” which prioritizes economic reforms over liberal political reforms and is characterized by a distinctively gradualist approach to development. A couple years later, Dani Rodrik advanced a similar model – “New Development Economics.” Similar to the East Asian Model, it emphasizes pragmatism and experimentation and considers China's post-Mao development as its principal exemplar. In this chapter, Pessimo (Dowdle) and Optimo (Prado) debate the merits and pitfalls of each of these instantiations of the idea of a consensus. We conclude – somewhat surprisingly – that it is in the discussion they generate, rather than in their substantive prescriptions, that the real value of these models lies. On Joshua Ramo's Original Idea of a “Beijing Consensus” Joshua Ramo introduced the term Beijing Consensus as a particular developmental strategy that was superior to the then still popular, but increasingly discredited, “Washington Consensus.” According to Ramo, the Beijing Consensus consists of “three theorems about how to organize the place of a developing country in the world.” We examine each theorem in turn. On Ramo's First Theorem “The first theorem repositions the value of innovation. Rather than the ‘old-physics’ argument that developing countries must start development with trailing-edge technology (copper wires), it insists that on [ sic ] the necessity of bleeding-edge innovation (fiber optic) to create change that moves faster than the problems change creates. In physics terms, it is about using innovation to reduce the friction losses of reform.” Pessimo on Development as “Bleeding-Edge” Innovation To me, this is just a buzzword salad. What does it mean to “create change that moves faster than the problems change creates”? What problems do copper wires cause that immediate transition to fiber optics outruns?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.088 |
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".