Bibliographic record
Abstract
A man's foes, it has been said, are those of his own household. Comparative Philology has suffered as much from its friends as from its opponents. (Sayce 1874–5:5) Introduction Scholars appear to agree that a successful demonstration of linguistic kinship depends on adequate methods. Unfortunately, there is disagreement and confusion concerning what these methods are, and hence discussions of methodology frequently assume a central role in considerations of possible remote relationships. Given this state of affairs, it is important to appraise the various methodological principles, criteria, and rules of thumb, as well as pitfalls, relevant to investigating distant genetic relationships. That is the goal of this chapter. We provide guidelines for both framing and testing proposals of distant linguistic kinship, and we point out the frequent errors that need to be avoided. (In Chapter 9 we evaluate several of the more prominent hypothesized distant genetic relationships on the basis of the methods surveyed here.) In practice, the successful methods for establishing distant linguistic affinity have not been different from those used to validate any family relationship, whether close or distant. The comparative method has always been the primary tool for establishing these relationships. Because the methods for investigating potential distant genetic relationships are not essentially different from those utilized to work out the history and classification of more closely related languages, this has resulted in a continuum from established and non-controversial families (e.g. Austronesian, Bantu, Indo-European, Finno-Ugric, Mayan), to more distant but solidly demonstrated relationships (e.g. Uralic, Siouan-Catawban, Benue-Congo), to plausible but inconclusive proposals (e.g. Indo-Uralic, Proto-Australian, Macro-Mayan, Niger-Congo), to doubtful but not implausible ones (e.g. Altaic, Austro-Tai, Eskimo–Uralic, Nilo-Saharan), and on to virtually impossible proposals (e.g. Basque–Na-Dene, Indo–Pacific, Mayan–Turkic, Miwok–Uralic, Niger–Saharan, and so on).
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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 | 0.033 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.013 |
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".