Insight from obsolescence: English demonstratives as a unique case for the study of doubling
Bibliographic record
Abstract
Several of the world’s languages exhibit double determination structures, including English dialects which have a construction with a demonstrative determiner and a locative adverb (e.g. this here book ). Doubling in demonstratives has commonly been explained as a language’s response to a loss of deixis, leading to a linguistic cycle . However, this explanation cannot be sustained for English because demonstratives are fully functioning grammatical deictics (e.g. this book ). In this article, we probe the role of doubling in the history and grammatical development of English double demonstratives with evidence from rural UK dialects. Using quantitative methods and the principle of accountability we calculate proportion of forms and patterning in simple and double demonstratives, enabling us to demonstrate that the doubled form has particular discourse-pragmatic functions, most notably, to flag topics in discourse. Our findings lead us to make two theoretical proposals. First, double demonstratives in English are used for discourse-pragmatic purposes; and second, doubling led to a new, complex determiner suitable to take over discourse-pragmatic functions from simple determiners ( complexification of the determiner paradigm ). Finally, we suggest that obsolescing features like the English double demonstrative offer key insights for understanding the development of linguistic systems.
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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 | 0.002 | 0.005 |
| 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 | 0.008 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".