Planetary health & COVID-19: A multi-perspective investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
COVID-19 can be characterized as an outcome of degraded planetary health drivers in complex systems and has wide-reaching implications in social, economic and environmental realms. To understand the drivers of planetary health that have influences of emergence and spread of COVID-19 and their implications for sustainability systems thinking and a narrative literature review are deployed. In particular, sixteen planetary health drivers are identified, i.e., population growth, climate change, agricultural intensification, urbanization, land use and land cover change, deforestation, biodiversity loss, globalization, wildlife trade, wet markets, non-planetary health diet, antimicrobial resistance, air pollution, water stress, poverty and weak governance. The implications of COVID-19 for planetary health are grouped in six categories: social, economic, environmental, technological, political, and public health. The implications for planetary health are then judged to see the impacts with respect to sustainable development goals (SDGs). The paper indicates that sustainable development goals are being hampered due to the planetary health implications of COVID-19.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it