Moving Beyond GDP and Achieving Our Common Agenda with Natural Capital Accounting
Bibliographic record
Abstract
The world has changed enormously over the past 80 years—we are richer and more interconnected than ever before, yet we also face unprecedented challenges, notably the climate and biodiversity crises. The Earth is hotter than it has ever been, with the warmest seven years occurring since 2015. The state of biodiversity is doing no better, with roughly a quarter of species assessed facing a high risk of extinction in the near future. Despite the brave new world that humanity now faces, one thing has remained steadfast over these past 80 years—our use of gross domestic product (GDP) in decision making. Gross domestic product is perhaps the most well-known and used statistic in the world. Virtually all countries compile GDP, which is derived from the System of National Accounts (SNA). However, over time GDP has been wrongly interpreted as a proxy for overall wellbeing and welfare rather than what it is—that is, a summary figure for economic activity. Unfortunately, this misuse of GDP has been at great peril, particularly to the environment.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".