Bibliographic record
Abstract
Stephen Pyne's (2015, 2021) accounts of fire history show that we have been changing the way we engineer fire. From the early days of open fires, we added furnaces for smelting, fireboxes for steam engines, and then the pistons and cylinders of internal combustion engines. All of these are about constraining and controlling the forces of combustion to make it serve our needs. Recently we have added fire engines, hoses, and preventative measures in terms of building codes, sprinkler systems, and fire hydrants to city streets too so we can better deal with things when they burn where they should not. Now we need to constrain fire still further so that we can control the large-scale consequences of our use of combustion. If we do so, in turn we will have a better chance of tackling the wildfires. Can we, as Naomi Klein (2014) suggested a decade ago, reimagine our cities in ways that take their energy consumption seriously and allow us to regain control of it from fossil fuel companies? Some cities and municipalities, faced with rising tides, damage from storms and citizens demanding action, are starting to declare states of climate emergency. They are beginning to think long and hard about how to both adapt to changes that are unavoidable, and act in ways that don't make things even worse. Can industrial societies also curtail the exploitation of resources from the lands of conquered peoples, and in the process rebuild energy systems that can give them economic futures and a life within their ecological contexts? This should help with the extinction crisis that we all face. Reworking our financial institutions to invest in sensible buildings, energy systems that do not require burning things, and remaking cities that are much healthier both because they have less pollution and can better cope with extreme weather, is a future worth working hard for; the Greta Thunberg generation deserve no less. Babcock Ranch in Florida, the community designed to run on solar power and to deal with extreme weather, which survived Hurricane Ian in 2022, points the way to building and planning sensibly for a climate-disrupted future.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".