Bibliographic record
Abstract
Natural assets are living dangerously: lacking natural owners they are liable to be plundered. Since mankind has had a long time in which to plunder, those depleting natural assets that are still around are there because they are difficult to extract. They lie beneath the earth, hence why they are called “subsoil assets.” Where are they? The world currently consists of 194 nation states, which can conveniently be grouped, as we’ve seen, into four roughly equal quadrants: the rich countries of the OECD; the countries of the bottom billion; Russia and China with their satellites; and the emerging market economies, such as India and Brazil. Each group occupies around a quarter of the planet’s land surface area. Occasionally national borders have been determined by the presence of subsoil assets. British colonial pioneers, for example, got wind of the existence of deposits of copper in central Africa and so pushed a railway line northward from South Africa. They found the copper belt in what is now Zambia. Having pushed over two thousand miles, however, they missed by some thirty the far richer copper deposits that now lie in the southeast corner of the Democratic Republic of the Congo. But usually, national borders do not reflect the endowments of subsoil assets to any significant degree. It would therefore be reasonable to regard subsoil assets as being randomly distributed between countries. Further, countries in the four groups are scattered across the planet. Although each group adds up to around a quarter of the planet’s total land area, it does not literally make up a quadrant, a neat quarter-slice out of a global orange. Since subsoil assets are randomly distributed among the 194 countries, and each of the four groups of countries is fairly randomly distributed around the earth, we might expect the law of large numbers to even out the distribution of subsoil assets among the groups. That is, while the random distribution over the 194 countries is likely to produce some spectacular differences between lucky and unlucky countries, by the time we have aggregated them into four massive groups the remaining differences should be much smaller.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".