Twenty actions for a “good Anthropocene”—perspectives from early-career conservation professionals
Bibliographic record
Abstract
Humans are now recognized as the main drivers of environmental change, leaving the future of our planet dependent on human action or inaction. Although the outlook of our planet is often depicted in a “doom and gloom” manner due to recent troubling environmental trends, we suggest that a “good Anthropocene” (in which human quality of life may be maintained or improved without cost to the environment) is attainable if we engage in adaptive, multi-disciplinary actions capable of addressing the socio-ecological issues of today and tomorrow. Early-career conservation scientists and practitioners have an unmatched understanding of novel technologies and social connectivity and, as those left with the ever-growing responsibility to be the problem solvers of the attributed increasing environmental consequences of living in the Anthropocene, their perspectives on steps towards a good Anthropocene are valuable. Here we present a list of 20 actions derived by early-career conservation scientists and practitioners for conservationists to help achieve a good Anthropocene that utilize the social connectivity and technology of today. Central to these actions are the notions that multi-, inter-, and trans-disciplinary collaboratives that embrace diverse world views need to be integrated into decision-making processes; training and outreach platforms need to communicate both environmental challenges and solutions broadly; and conservation successes need to be acknowledged and disseminated in a forward-looking, adaptive capacity. Together the 20 actions identified here reinforce the underlying paradigm shift that must accompany living in the Anthropocene, given that biodiversity and healthy ecosystems are requisite for sustained human life. By sharing this list of actions, we look to promote positive socio-environmental changes towards the collective goal of achieving a good Anthropocene.
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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 | 0.036 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".