Bibliographic record
Abstract
This book is different and seeks to fulfil an important gap in knowledge and even thinking. The map of the world which is traditionally learned by youngsters in school is a flat one which has Europe at the centre, Russia to the right and Canada to the left. What is not self evident is that the distance between North America and Russia is only thirty miles across the Bering Straits. Add to this the fact until the arrival of the Arno Peters projection with digital technology, we had to make a tradeoff between the needs of navigation and the physical size represented of the individual countries which might then fuel political claims of territorial ownership. Yet land size is but one major factor, another is population and this is starts to explain the need for this book. Asia represents the two largest populations in the world: China and India. Now, many books have focused on the rise of these two countries but this one takes a different approach. We seek to focus on Asia not just because it contains the world’s two most populous countries but because these two countries and continuing to expand economically at the same time. With the compounding effect of high successive levels of annual economic growth, we start to see the true potential of Asia and the need to reconsider traditionally held views.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".