Impacts of Meeting Minimum Access on Critical Earth Systems amidst the Great Inequality
Bibliographic record
Abstract
The UN 2030 Agenda includes 17 Sustainable Development Goals towards improving access to resources and services, reducing environmental degradation and bringing down inequality. However, there is debate on the magnitude of the environmental burden that would arise from meeting the needs of the poorest, especially compared to much larger burdens from the rich. We first show that the ‘Great Acceleration’ of human impacts is characterized by a ‘Great Inequality’ in utilising and damaging the environment. We then operationalize ‘just access’ to minimum energy, water, food and infrastructure. Third, in an unequal world, we show that hypothetically meeting ‘just access’ would add 2-26% to current impacts on the Earth’s natural systems of climate, water, land and nutrients. These additional impacts, hypothetically caused by about a third of humanity, equal those currently caused by the wealthiest 1-4%. Nevertheless, achieving ‘just access’ calls for redistribution within stable Earth System Boundaries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".