Bibliographic record
Abstract
Abstract Languages can be similar in many ways - they can resemble each other in categories, constructions and meanings, and in the actual forms used to express these. A shared feature may be based on common genetic origin, or result from geographic proximity and borrowing. Some aspects of grammar are spread more readily than others. The question is - which are they? When languages are in contact with each other, what changes do we expect to occur in their grammatical structures? Only an inductively based cross-linguistic examination can provide an answer. This is what this volume is about. The book starts with a typological introduction outlining principles of contact-induced change and factors which facilitate diffusion of linguistic traits. It is followed by twelve studies of contact-induced changes in languages from Amazonia, East and West Africa, Australia, East Timor, and the Sinitic domain. Set alongside these are studies of Pennsylvania German spoken by Mennonites in Canada in contact with English, Basque in contact with Romance languages in Spain and France, and language contact in the Balkans. All the studies are based on intensive fieldwork, and each cast in terms of the typological parameters set out in the introduction. The book includes a glossary to facilitate its use by graduates and advanced undergraduates in linguistics and in disciplines such as anthropology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".