Bright spots: seeds of a good Anthropocene
Bibliographic record
Abstract
The scale, rate, and intensity of humans’ environmental impact has engendered broad discussion about how to find plausible pathways of development that hold the most promise for fostering a better future in the Anthropocene. However, the dominance of dystopian visions of irreversible environmental degradation and societal collapse, along with overly optimistic utopias and business‐as‐usual scenarios that lack insight and innovation, frustrate progress. Here, we present a novel approach to thinking about the future that builds on experiences drawn from a diversity of practices, worldviews, values, and regions that could accelerate the adoption of pathways to transformative change (change that goes beyond incremental improvements). Using an analysis of 100 initiatives, or “seeds of a good Anthropocene”, we find that emphasizing hopeful elements of existing practice offers the opportunity to: (1) understand the values and features that constitute a good Anthropocene, (2) determine the processes that lead to the emergence and growth of initiatives that fundamentally change human–environmental relationships, and (3) generate creative, bottom‐up scenarios that feature well‐articulated pathways toward a more positive future.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".