COVID-19, global health and climate change: Causes and convergences
Bibliographic record
Abstract
Despite massive global economic growth and advances in science and medicine with spectacular aggregate and individual improvements in health and life expectancy over the past century, the world has now become severely unstable in multiple domains – biological, sociological, political, ecological, economic, and health care. These pervasive instabilities are organically interactive within a complex world system that has reached crisis status at local, global, and planetary levels. Lying at the heart of this complex crisis are long-neglected disparities in health and well-being within and between countries, the refusal to face how these and climate change have arisen, and how economic considerations have fuelled the trend towards entropy (gradual decline of the planet into disorder). The critical point we have reached, starkly highlighted by the emergence of the COVID-19 pandemic pari passu with ongoing climate change and planetary degradation, reminds us of our global interconnectedness with each other and with nature. Comprehending and acknowledging the myriad, humanly constructed forces in each of these domains influencing all aspects of life, are the first steps towards effectively facing challenges to our health, our humanity (collectivity as humans) and our planet. Overcoming denial, acknowledging the magnitude and complexity of these challenges, prescient vision and dedicated action capable of fostering the cooperation for overcoming obstacles are now vital to seeking peaceful pathways towards more equitable and sustainable lives. South Africa is a microcosm of the world, with its local threats and challenges mirroring the global. Significance: Instabilities that pervade the world, highlighted by the COVID-19 pandemic, are especially significant for South Africa, where they manifest most starkly because of its apartheid legacy, its relative success economically on the African continent, and the implications of ongoing widening disparities and antagonism amongst South Africa’s diverse people. Belief in moving towards narrowing wide disparities through decolonisation and reversion to an ‘idyllic African heritage’ via a transformation that includes widespread corruption, and the ANC government’s perverse erosion of lives today and in the future through ‘state capture’, intensifies rather than ameliorates our predicament in an era when cooperation and a clear vision of current threats and future possibilities are desperately needed. In an accompanying article, potential pathways towards a better future are offered through suggested shifts in paradigms of thought and action.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".