Bibliographic record
Abstract
“People know what they do; they frequently know why they do what they do; but what they don't know is what what they do does.” Michel Foucault, Madness and Civilization The Promethean moment Since life first emerged from oceans and plants started growing on land surfaces fires have had fuel. It's so obvious that we don't often think about it, but stuff that was once living is what burns. Trees provide firewood. The paper we use to start fires in our coal or wood burning stoves comes from trees. If we use fire starters instead of paper most of them are petroleum based. And petroleum is in fact a deeply buried residue of former life. Mostly we burn fossil fuels. Coal, petroleum and natural gas all come from the remains of decayed living things. Organic material, stuff that was once living, has lots of carbon in it, and burning it produces carbon dioxide gas. Once it collects in the atmosphere it traps heat and warms the world. Oxygen is a by-product of life too, and it is also what is needed to make fire. That and a spark to start a flame so that the process of combining the carbon from organic stuff, things that were once alive, with oxygen in the air, can happen. We will never know exactly how the human use of fire started, but it is a reasonable guess that it started in various places in different ways. What matters, as fire historian Steven Pyne (2021) makes very clear, is that, unlike any other species we learned to start fires. Yes, we have language, and culture, and tools, and religions and lots of other things that we think separate us from other species. But beavers are great hydrological engineers; elephants have complicated communication systems; whales too. Ants and other insects build complex structures. Only us humans start fires. Once we learned that “ignition trick” in Pyne's apt phrase, we could have fires pretty much where and when we wanted them. It is difficult in the rain for sure and fires are hard in a desert or on Arctic ice sheets, far from plants to use for fuel. But nonetheless we could have heat where we needed it much of the time, and that made a difference, a very big one as it turned out.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".