Bibliographic record
Abstract
Despite (or perhaps because of) the staggering scale and pace of global change, the concept of the Anthropocene eludes discrete classification. There is widespread consensus that conditions associated with the Anthropocene, including rapid biodiversity loss and climate change, must be addressed if we are to enjoy ongoing and rich experiences. At the crux of human impacts is urban living – as of 2024 nearly 60% of people live in cities. Human societies are tightly interconnected with each other and surrounding ecosystems, but for city-dwellers, these connections may seem abstract. A failure to appreciate and foster such connections can have human and environmental health repercussions. We present a concept for a meal featuring local wild foods that could only be appropriately served under regionally ameliorated Anthropocene conditions. By presenting this hypothetical “solution”, we seek a common ground that spans human (and non-human) cultures and behaviors, and a concept that can be extended to any community. The simplicity of the “Anthropocene meal” belies three primary challenges: improvements to urban design, maintenance of ecosystem health, and shifting cultural attitudes. However, these barriers are quantifiable and may be addressed within annual to decadal timelines, making the Anthropocene meal a broadly achievable goal, and thus a valid source of optimism in a time of great uncertainty.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".