Bibliographic record
Abstract
The definitions of “good” and “bad” typically belong to the field of ethics. Does physics have a property that can distinguish and group “good” and “bad” events using only its own physical instruments and equations? Although the physical property “entropy” as a measure of chaos appears to be the prime candidate for such a grouping, it is in fact unsuitable for a detailed analysis. However, the “Entropic Potential of an Event”, which describes the influence of the current event to the future change in entropy, perfectly suits this role. While the second law of thermodynamics dictates the direction of entropy change in an isolated system it does not dictate the speed of entropy growth. The speed of entropy growth on Earth is changing in particular due to human-related events, which ethics calls "good" or "bad". Such events, which ethics defines as "good", always eventually decelerate entropy growth. In contrast, events which ethics defines as "bad", always eventually accelerate entropy growth. This article presents the methods of calculating the “Entropic Potential of an Event” for the cases: “A commander receives an order to bombard a city”, “A cancer tumor is growing inside a human’s body” and other. This article also stresses the importance of the “Time factor” since only for a sufficiently large time interval T the “En-tropic Potential of an Event” Z(T, A) can be estimated and potentially precisely calculated. This article also checks on a scale of several centuries if real-life events are averaged in such a way that their entropic potentials become negligible. This analysis shows that prior 1750 the averaged entropic potential of events occurred in human society was negative and had the value of approximately 10^17 bits (value is an initial approximation). The situation after the industrial revolution is more complicated, as past 1750 human civilization began to utilize non-renewable resources (oil, coal, gas, etc.) for warming, cooling, and transportation with a corresponding entropy growth. The term “entropy” is applicable to a very wide range of events, from physics and chemistry to art and information. Correspondingly, the significant advantages of the “Entropic Potential of an Event” as a physical foundation of the intuitive terms “good” and “bad” is its measurability, ability to compare events of entirely different natures and its universality Weak point of "Utilitarianism" is inability to quantify, compare, or measure happiness or well-being which is postulated to be the ultimate goal of moral behavior. The "Entropic Potential of an Event" solves this problem and presents in formulas (and sometime calculates in bits) the Entropic Potentials of events we normally label as "good" and "bad".
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".