Utilizing basic income to create a sustainable, poverty-free tomorrow
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic of 2020 was a reminder of society's vulnerability in the face of natural upheavals, leading to widespread unemployment and increased poverty. Simultaneously, human activities have precipitated large-scale environmental degradation and catastrophic climate change. Here, we conduct a global-scale, 186-country analysis examining the potential impact of basic income (BI) as a two-pronged solution to both sustainability and social resilience. We reveal BI's potential to bolster economies, particularly in times of crisis. To lower the huge barrier imposed by implementation costs, we suggest a diverse array of strategies aimed at financing BI, strategically designed to concurrently alleviate economic insecurity while fostering nature conservation. We suggest that BI implementation is feasible and could be a potent tool in addressing the twin challenges of decreasing worldwide poverty while reducing environmental degradation—a nexus that arguably constitutes the paramount global challenge of our times.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".