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Record W1525468600 · doi:10.1017/cbo9780511486906.008

How to show languages are related: the methods

2008· book-chapter· en· W1525468600 on OpenAlexaff
Lyle Campbell, William J. Poser

Bibliographic record

VenueCambridge University Press eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.074
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0040.020
Scholarly communication0.0100.016
Open science0.0040.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0740.013

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.

Opus teacher head0.046
GPT teacher head0.290
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicGender Studies in LanguageFrench-language works237,207