Anthropocene Crisis: Climate Change, Pollinators, and Food Security
Bibliographic record
Abstract
In this paper, we propose a new approach—understood as a whole-of-community approach—to address a dualistic and dysfunctional human/nature relationship. Of particular concern is the decline in health and numbers of the insects that pollinate an estimated 90 percent of the Earth’s flora and an estimated 35 percent of global crop volume. Specifically, bees provide the majority of biotic pollination and play a critical role in food crop pollination. Multiple factors are contributing to this growing problem including a changing climate. In 2016, the International Commission on Stratigraphy agreed that the concept of the Anthropocene—the human epoch—is of sufficient scale to be considered part of the geological time scale. This indicates that these crises are not random or passive—they are largely the direct result of human activities. Despite decades of awareness of these socio-ecological issues, they continue to worsen. In addition, the growing awareness of the critical role of pollinators is creating a new understanding of our interconnectedness with the “natural” world. We introduce the Bee City movement as a way to operationalize a whole-of-community approach. Individual action is critical, but addressing pollinator health in these forums legitimizes and provides an institutional space for otherwise fringe, or even marginalized, activities and more coherent spaces for habitat creation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".