Bibliographic record
Abstract
There is a widespread tendency for diverse uses of Nature, on scales from small and local to very large, to become unsustainable. Once unsustainable, bringing a use back to sustainability and keeping it sustainable then takes substantial effort and tools appropriate to the context of the use. This Perspective first asks why is the tendency for unsustainability so pervasive, when it is an outcome that no user group has adopted as an objective, and ways to keep uses sustainable are known. I argue and present evidence that the common factor underlying the pervasiveness of unsustainable uses of Nature is inequity in the distribution of the benefits created from those uses, with both the wealthy “winners” of the distributional inequities and those disadvantaged and in poverty driving uses toward increasing unsustainability in ways that depend on the nature of the inequities. Unless the inequity of distribution of benefits from uses of Nature is addressed as an issue in its own right, there are few or no pathways to medium or long-term sustainable use. However, if inequity is addressed broadly and effectively, many pathways are available and societies can select the pathways appropriate to their cultural and ecological contexts.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".