Bibliographic record
Abstract
Infodynamics, for our purposes, is a developmental perspective that animates information theory by way of thermodynamics. The isomorphism between Boltzmann's statistical interpretation of physical entropy as disorder and Shannon's formulation of variety as informational entropy signals a deep connection between information and entropy production. Information is any configuration that might have been different, providing that it delays energy dissipation so that the energy is dissipated more completely. The entropy production of individual dissipative structures increases at first but eventually decelerates. I consider the questions: why do these structures grow? And why don't they keep on growing? As the universal expansion of the Big Bang accelerated, matter precipitated from disequilibrated energy. In its own search for equilibrium, matter clumped, signaling further disequilibrium. The only way these clumps can be destroyed is by others, and this role of gradient degradation entrained the evolution of complexity, all the way to living systems. This serves universal equilibration because, generally, more of an energy gradient must be lost as heat than can become reembodied in its consumers, and so it can be said that these structures grow to serve gradient degradation, taking the second law of thermodynamics as a final cause. I suggest that energy degradation is harnessed by growing systems because that process allows the fastest eventual dissipation in the direction of the lowest grade of energy. Three stages of development of dissipative structures are described: immature, mature, and senescent. Growth is limited by senescence, which I take to be a consequence of information overload. I suggest that ecosocial systems harnessed by human population growth impose less information on ecological transformations than do typical mature ecosystems, thereby tapping more powerful energy flows and producing more wastes of a higher grade than heat, which act as pollutants. Warfare is interpreted as a mechanism to prevent ecosocial senescence. I suggest that ecosocial systems should be planned in the direction of maintaining system maturity as long as possible.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".