Bibliographic record
Abstract
First of all, it is a great pleasure to be here. Thank you for inviting me. Given that communicating from a far is not the easiest thing to do, what I have decided to do is to give a quick overview of the arguments that have emerged from the book that James and I wrote. In fact, this book is a synthesis of about 16 years of research that James and I did. I think it is fair to say that a lot of economic development and economic growth is motivated by patterns that are reported in the book. In particular, this is data from Angus Madison’s life’s work, which is not entirely uncontroversial, but the overall pattern here is fairly uncontroversial. The patterns that we observe have actually been in the background of many attempts to understand long patterns of economic development. I think they also point out that it is going to be very difficult to understand why certain parts of the world that were either on par with, say, Asia, in particular the Indian Subcontinent and China, have increased their income per capita and their prosperity so much in 500 years leading to today, particularly from the period around early 1800s to essentially to the end of the World War II, where there is this big divergence taking place. The trends in economic development show that United States of America, Canada, New Zealand and Australia have pulled so much ahead of, say, Asia, where both India, the Indian Subcontinent in this case, and China more or less show the same picture, where there is not much growth going on until the end of the World War II.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.376 | 0.195 |
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".