Bibliographic record
Abstract
Philosophers and scientists of the eighteenth-century Enlightenment believed that by increasing knowledge of how the world works, humans would be liberated from superstition, and that reason would follow learning. However, in its engagement with science, modern environmentalism appears to have turned back the clock on this view of Enlightenment. While environmentalism was born into science and technical expertise, it matured in an era of skepticism, and few believe that science and technology will enable humanity to become effective planetary stewards. As Jasanoff observes, Rachel Carson’s seminal broadside Silent Spring (1962) is credited with helping to ignite a social movement. This led to important studies and changes in environmental laws and policies throughout the 1970s. But despite this, backlash against environmental expertise, especially in the United States, gained momentum, and this backlash continues to this day. While advancements in environmentalism has made large strides and impacted policies – such as the Montreal Protocol, an international pact to phase out production of fluorocarbons – the story of climate science traces a less triumphalist narrative line. In turn, political action on climate change has failed to keep up with the urgency of scientific predictions. Most recently, with President Trump pulling out of the Paris Agreement, due to economic rather than environmental reasons. In this chapter, Jasanoff outlines how the events of the past half-century have taught us that gains in scientific understanding will not translate into wise policies for the human future. Instead, the politics of environmental science in the next half-century will have to build on the understanding that science and planetary stewardship are co-produced.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.058 |
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".