The “Great Divergence,” Politics, and Capitalism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proceeding from a critical assessment of two recent books, Prasannan Parthasarathi’s Why Europe Grew Rich and Asia Did Not , and Jean-Laurent Rosenthal and R. Bin Wong’s Before and Beyond Divergence , this paper takes stock of the present state of the “Great Divergence” debate. It is argued that the discussion needs to be refined to distinguish between levels of economic development, and paths or trends, in the eighteenth century as well as between causes of sustained growth, and of stagnation or decline in the nineteenth century. It is further suggested that the debate needs to be connected to an understanding of the causes of a “Great Convergence” in the early modern world, and how different regions might have reached similar levels of economic complexity, but might nevertheless have been on different paths for future growth. Finally, this paper suggests that the divergence debate also needs to be connected to the debate on the transition to capitalism.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it