Exploring Alternative Futures in the Anthropocene
Bibliographic record
Abstract
Many challenges posed by the current Anthropocene epoch require fundamental transformations to humanity's relationships with the rest of the planet. Achieving such transformations requires that humanity improve its understanding of the current situation and enhance its ability to imagine pathways toward alternative, preferable futures. We review advances in addressing these challenges that employ systematic and structured thinking about multiple possible futures (futures-thinking). Over seven decades, especially the past two, approaches to futures-thinking have helped people from diverse backgrounds reach a common understanding of important issues, underlying causes, and pathways toward optimistic futures. A recent focus has been the stimulation of imagination to produce new options. The roles of futures-thinking in breaking unhelpful social addictions and in conflict resolution are key emerging topics. We summarize cognitive, cultural, and institutional constraints on the societal uptake of futures-thinking, concluding that none are insurmountable once understood.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".