Logos Reconstructed: On the Ideal of Adam’s Originally Perfect Language and Recovering its Semiotic Realism
Bibliographic record
Abstract
Abstract Umberto Eco in The Search for the Perfect Language explores the ‘dream of a perfect language’ that has sought to recapitulate the lost perfection of Adam’s original language. Humanity is seen as forgetful of the preternatural knowledge once contained in a transparent language that perfectly identified essences. Eco’s historical narrative of this pursuit, labeled “a series of failures,” is examined first. Then, Leibniz’s Adamicism is explored, which asserts that a language can be Adamic if it mirrors the natural and non-arbitrary qualities of Adam’s language. Cross-culturally, Sanskrit realism and Plato’s natural-name thesis support this, emphasizing the connection between words and meanings. Following this, the criteria for linguistic perfection (◊P) are established, relying on five necessary assumptions (A) concerning ontology, epistemology, accessibility, translatability, and intersubjectivity. This paper defends reconstructing an ideal language without seeking to return to the forever lost mother tongue. Instead, it assesses the potential for our current system-of-signs to regain semiotic realism and represent reality accurately. A thought experiment justifies returning to semiotic realism, examining the potential of revealing the hidden phenomenology of logos – the universal reason underlying all languages. Conclusively, this project rejects empirical nominalism and explores accessing the preternatural knowledge of necessary and immutable ideas, lost after the fall and Babel’s catastrophe.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".