Bibliographic record
Abstract
Over the last decade the Great Divergence, or the timing of when the wealth gap between the Western world and the Rest of the world opened up, has become a prominent issue in the discipline of economic history. The debate has been conducted at a macro-economic level, however, and business historians have made hardly any contribution. They have made a potentially richer contribution to the less explored question of why the Rest failed to catch up after the gap had opened up, though most of this literature has not been structured in terms of the Great Divergence. This chapter begins with these two debates before turning to the Great Convergence of the last three decades. By 2017 China was the world’s second largest economy. It accounted for nearly 15 percent of world GDP. Asia as a whole accounted for 34 percent of world GDP; the United States and Canada for 28 percent; and Europe for only 21 percent (World Economic Forum, 2017). While many developing economies, especially in Africa, were still desperately poor compared to the West, the scale and speed of the Great Convergence was nevertheless striking.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".