What can we learn about the earliest human language by comparing languages known today?
Bibliographic record
Abstract
‘Proto-World’ Conjectural protolanguage from which, according to some applications of mass comparison, all later languages have developed. (P. H. Matthews, The concise Oxford dictionary of linguistics . 1997:302) Introduction Looking back from modern and attested older languages, what can we find out or reasonably hypothesize about the earliest human language (or languages)? The origin and evolution of human language is currently a very active area of scholarship, though curiously there appear to be more hypotheses than facts. That is, in spite of some very clever recent thinking in various directions, there is little of real substance from the remote past to work with, leaving speculation to dominate. Nevertheless, one area in which concrete data have been explored in language origins research is comparison of lexical and structural material from known languages. Attempts to understand something of the origin and evolution of the earliest human language are of relevance to the goals of this book because many involve very long-range classifications of the world's languages and claims about distant genetic relationship. In this chapter we deal with the lexical data which some scholars have used in attempts to reach conclusions about the earliest human language, and also less directly with some structural traits. The goal of the chapter is to determine what, if anything, can be learned about the earliest human language or languages based on comparisons of the linguistic evidence extant in modern and older attested languages.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.037 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".