Causal Leverage Density: A Universal Framework for Semantic Information
Bibliographic record
Abstract
Despite living in the information age, we often ponder: what does information mean? Shannon defined syntactic information using a simple measure applied to probability distributions. Syntactic information captures the degree of ‘surprise’ on the part of a message receiver, given some prior credences over the alphabet of symbols being transmitted through a channel; however it remains silent on semantics—the notion of meaning. This gap is especially critical for the artificial life community, which aims to understand and synthesize life-like processes where meaning and correlated function are essential. Existing approaches to semantic information are often domain-specific, tied either to linguistic contexts or to the viability of agents, limiting their generality. We thus introduce ‘Causal Leverage Density’ (CLD), a generalised approach to quantifying semantic information grounded in established concepts from statistical physics. CLD quantifies the influence of syntactic information by evaluating the effect of information-scrambling interventions on the future evolution of a system’s phase space trajectories, which encapsulate all relevant degrees of freedom. This concept yields a universal approach to characterising meaning and semantic influence in any type of system, from physics to biology to machine learning. Crucially, by identifying systems where information has causal efficacy, CLD offers a robust tool for defining and detecting life as might be found elsewhere in the universe or created artificially on Earth.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".