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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.086 | 0.008 |
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; both teacher heads agree on what is shown here.
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".