Universal Properties of Human Language in the Light of Natural Phenomena
Bibliographic record
Abstract
The investigations of linguistic literature show that linguists today are much inspired by the goals of generative grammar, as well as the important human universals which explain child language acquisition, the universals of general cognition and learning. To put it the other way, linguists are interested in studying the human language to discover the nature of Universal Grammar the principles of which characterize all human languages and this major aim becomes the basis of contemporary linguistic theory of today. As D. Crystal writes: “… the main task of the linguistic scholar is basically to study and understand the general principles upon which all languages are built” (Crystal, 1977). In this respect we are sure that this article may throw some light on the origin of universal properties of human language. Our aim, accordingly, is to study the origin of general principles of human language and to discover natural factors which cause the human language to have universal properties. In other words, our aim is “to find out the laws of human language” in relation to natural phenomena. In this connection an attempt is made to describe variety of observable similar facts and compare them both pertaining to human language and natural processes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".