Review of Byron Williston, The Anthropocene Project: Virtue in the Age of Climate Change . Oxford University Press, 2015. 209 pages
Bibliographic record
Abstract
Byron Williston has titled his thoughtful and engaging book on virtue ethics and climate The Anthropocene Project. Here Williston is grappling with an issue that vexes most of us. The science of anthropogenic climate change has been increasingly clear, at least since the IPCC First Assessment Report in 1990. But, in the intervening twenty-five or so years we’ve done damn little about it. We have responded to climate change with dithering and doubt, not concrete action. Why is this? Williston suggests that it is because we are bad people—well, perhaps not exactly bad but just not very good. Grappling with climate change, he argues, will require us to become better, more virtuous people. As he tells us: “If we are going to find a morally defensible path through the climate crisis we need to become better people, and that means cultivating the virtues”. To glean the wisdom needed to cope with climate change, the specific virtues of hope, truthfulness and justice will need cultivation.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".