Comparative linguistics of other language families and regions
Bibliographic record
Abstract
In the distant past, no one could speak, which is one reason that people were destroyed at the end of the First and Second Creations. Then, while the sun deity was still walking on the earth, people finally learned to speak (Spanish), and all people everywhere understood each other. Later the nations and municipios [towns] were divided because they had begun to quarrel. Language was changed so that people would learn to live together peacefully in smaller groups. (Tzotzil oral tradition, Gossen 1984:46–7) Introduction Much of the discussion of how language families are established so far has involved the history of Indo-European research, appropriately so, given its role in the development of comparative linguistics. In this chapter, we survey how several other important language families came to be established. The particular language families discussed are well known, universally accepted, were for the most part established relatively early in the history of linguistics, and so potentially had some impact on the development of the historical linguistics. We examine the methods used to establish these families in order to determine what criteria and principles were involved and what lessons we can take from them. We also consider language classification in Africa, Australia, and the Americas, with an eye towards the methods utilized in language classification in these regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".