Inclusive wealth in the twenty-first century: a summary and further discussion of Inclusive Wealth Report 2018
Bibliographic record
Abstract
It is increasingly common to judge the sustainable development of nations by non-declining social well-being. Determinants of social well-being have been measured and used for sustainability analysis. In particular, inclusive wealth per capita, which comprises produced, human, and natural capital, was reported in the Inclusive Wealth Report in 2012 and 2014. Here, we report the updates of the third edition of the report, which covers 140 countries from 1990 to 2014. In per capita terms, only 60% out of 140 countries show non-declining wealth for the past quarter century. Most countries, both developed and developing, fall into the group of running down natural capital and increasing produced and, to a lesser extent, human capital. We also include fishery stock as part of natural capital, and we find that only a few countries have increased their fishery capital in the studied period. Inclusive wealth has typically grown much less than GDP per capita and does not resemble change in other development indices. Globally aggregated produced and human capital per capita increased 94% and 28%, respectively, while natural capital per capita declined by 34%. In 2014, produced, human, and natural capital account for 24%, 64%, and 11%, respectively, which is similar to the recent findings by the World Bank. In addition, inclusive wealth is approximately 12 times GDP on average, much higher than conventional wealth-income ratios globally, and is at least at par with those ratios in high-income countries under financialization.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".