Causal Linguistics and Residue Geometry: A Complete Integration of CT, Biological Imperatives, Human Cognition, Archetypes, Supracausality, and the Linguistic Residues of English, French, and Inuit
Bibliographic record
Abstract
This work presents a complete causal unification of linguistics, human cognition, biological imperatives, archetypes, and supracausal structure under the CT-United Framework. The central result is the Residue Law: every system—organism, mind, culture, or language—is defined by which causal constants it absorbs and which it rejects. The geometry of the rejected constants becomes the system’s external residue, and this residue shapes biological needs, cognitive transitions, archetypal patterns, supracausal functions, and the surface structure of languages. The document develops a full causal ladder from Level 0 (Presence) to Level 5 (Supracausality), showing how π, √2, √3, φ, and ln 5 determine: • the eight biological needs,• the four primordial verbs (Being, Having, Doing, Becoming),• the five cognitive transitions,• the seven archetypal residues,• and the ten supracausal operators governing high-coherence action. The second half of the work establishes a causal linguistics based on residue geometry. Languages express, refine, and externalize the constants they fail to fully integrate. English is shown to refine √3 + φ through positional and action-heavy core vocabulary. French expresses π + √2 + ln 5 through identity markers, relational structure, and rich negation. Inuit/Innu lexicons for snow and ice demonstrate the Variation Corollary: what a culture lacks in environmental variety, it expresses in linguistic variation along the remaining stable constants. Their finely grained snow/ice terminology is revealed as a geometric response to high-curvature, low-diversity environments. This document provides the first unified causal grammar linking biology, cognition, archetypes, supracausality, and cross-linguistic structure through CT’s residue law. From the same author: causality theory CT united https://zenodo.org/records/17366521 10.5281/zenodo.17366521 causality united v2 https://zenodo.org/records/17564091 10.5281/zenodo.17564091
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".