Bibliographic record
Abstract
Adding to a growing body of knowledge about how the social-ecological dynamics of the Anthropocene affect human health, this collection presents strategies that both address core challenges, including climate change, stagnating economic growth, and rising socio-political instability, and offers novel frameworks for living well on a finite planet.Rather than directing readers to more sustainable ways to structure health systems, Health in the Anthropocene navigates the transition toward socialecological systems that can support long-term human and environmental health, which requires broad shifts in thought and action, not only in formal health-related fields but in our economic models, agriculture and food systems, ontologies, and ethics.Arguing that population health will largely be decided at the intersection of experimental social innovations and appropriate technologies, this volume calls readers to turn their attention toward social movements, practices, and ways of living that build resilience for an era of systemic change.Drawing on diverse disciplines and methodologies from fields including anthropology, ecological economics, sociology, and public health, Health in the Anthropocene maps out alternative pathways that have the potential to sustain human well-being and ecological integrity over the long term.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.913 | 0.860 |
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".