Beyond Science and Technology: Creating Planetary Health Needs Not Just ‘Head Stuff’, but Social Engagement and ‘Heart, Gut and Spirit’ Stuff
Bibliographic record
Abstract
I have been involved in studying and working within what is now called the Anthropocene for almost 50 years, and in all that time, not only have we failed to make much progress, but the state of the Earth’s ecosystems has generally worsened. Yet somehow we must create a world in which everyone on Earth has good health and a good quality of life—a matter of social justice—while living within the physical and ecological constraints of the one small planet that is our home; this is the focus of the new field of planetary health. Our worsening situation is not due to lack of knowledge, science and technology; in broad terms, we knew most of the challenges and many of the needed solutions back in the 1970s. Instead, the challenges we face are social, rooted in cultural values, political ideologies, legal and economic systems, ethical principles and spiritual/religious beliefs. Therefore, we have to move beyond science and technology and address these broader socio-cultural issues by engaging in economic, legal and political work, complementing and supplementing ‘head stuff’ with ‘heart, gut and spirit stuff’, and working from the grass roots up.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".