A multispecies coexistence based on rewilding and degrowth for the sake of global health
Bibliographic record
Abstract
Abstract The climate emergency is closely linked to biodiversity loss, both in its (anthropogenic) causes (anthropogenic) and possible (nature-based) solutions. Both crises are a major threat to global health because zoonoses, global warming and other climate impacts make present and future generations vulnerable, and aggravate social inequalities. Indeed, this has led some authors to start talking about ecological determinants of health. However, based on a narrative review of recent academic literature, I will argue that trophic rewilding strategies, framed within conservation biology, can ensure global health by stopping the spread of zoonoses and animating the carbon cycle. A first aim of my contribution will be to defend this correlation. Then, my next purpose will be to address how rewilding could favor coexistence with protected or reintroduced keystone species. The recent UN Biodiversity Conference (COP15) in Montreal agreed to extend the care of wilderness areas and restore 30% of degraded ecosystems by 2030, but this is a political as well as a social challenge. The current development model in Europe, based on fossil-dependent economic growth, is a stumbling block for wildlife to flourish. I will therefore argue that degrowth could be a complementary development alternative to rewilding in order to make a more serious commitment to global health. Key messages • The ecological restoration of trophic rewilding can contribute to global health by mitigating climate change and zoonoses. • The development of European societies is based on economic growth, and as this is a threat for wildlife flourishing, a shift towards degrowh could complement rewilding and strengthen global health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".