Bibliographic record
Abstract
There is a long tradition of thinking of thermodynamics, not as a theory of fundamental physics (or even a candidate theory of fundamental physics), but as a theory of how manipulations of a physical system may be used to obtain desired effects, such as mechanical work. On this view, the basic concepts of thermodynamics, heat and work, and with them, the concept of entropy, are relative to a class of envisaged manipulations. This view has been dismissed by many philosophers of physics, in my opinion too hastily. This paper is a sketch and defense of a science of manipulations and their effects on physical systems. This is, I claim, the best way to make sense of thermodynamics as it is found in textbooks and as it is practiced. I call this science thermo-dynamics (with hyphen), or \ΘΔcs, for short, to highlight that it may be different from the science of thermodynamics, as the reader conceives it. Even if one is not convinced that it is the best way to make sense of thermodynamics as it is practiced, it should be non-controversial that ΘΔcs is a legitimate science. An upshot of the discussion is a clarification of the roles of the Gibbs and von Neumann entropies. Given the definition of statistical thermo-dynamic entropy, it can be proven that, under the assumption of availability of thermodynamically reversible processes, these functions are the unique (up to an additive constant) functions that represent thermo-dynamic entropy. Light is also shed on the use of coarse-grained entropies.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".