Bibliographic record
Abstract
In this chapter I urge a fresh look at the problem of explaining equilibration. The process of equilibration, I argue, is best seen, not as part of the subject matter of thermodynamics, but as a presupposition of thermodynamics. Further, the relevant tension between the macroscopic phenomena of equilibration and the underlying microdynamics lies not in a tension between time-reversal invariance of the microdynamics and the temporal asymmetry of equilibration, but in a tension between preservation of distinguishability of states at the level of microphysics and the continual effacing of the past at the macroscopic level. This suggests an open systems approach, where the puzzling question is not the erasure of the past, but the question of how reliable prediction, given only macroscopic data, is ever possible at all. I suggest that the answer lies in an approach that has not been afforded sufficient attention in the philosophical literature, namely, one based on the temporal asymmetry of causal explanation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.022 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".