Bibliographic record
Abstract
Sources Maps Acknowledgements 1. English dialect grammar 2. Pronouns and pronominal systems in English dialects 3. The personal dative in Appalachian speech, Donna Christian 4. Demonstrative adjectives and pronouns in a Devonshire dialect, Martin Harris 5. The actuation problem for gender change in Wessex versus Newfoundland, Harold Paddock 6. Verb systems in English dialects 7. Variation in the use of ain't in an urban English dialect, Jenny Cheshire 8. Double modals in Hawick Scots,Keith Brown 9. On grammatical diffusion in Somerset folk speech, Ossi Ihalainen 10. Variation in the lexical verb in inner-Sydney English, Edina Eisikovits 11. Aspects in English dialects 12. Periphrasic do in affirmative sentences in the dialect of East Somerset, Ossi Ihalainen 13. Preverbal done in Alabam and elsewhere, Crawford Feagin 14. Conservatism versus substratal transfer in Irish English, John Harris 15. Non-finite verb forms in English dialects 16. Transitivity and intransitivity in the dialects of the south-west of England, Jean-Marc Gachelin 17. Toward a description of a-prefixing in Appalachian English, Walt Wolfram 18. A grammatical continuum for (ing), Ann Houston 19. Adverbials in English dialects 20. Affirmative any more in present-day American English, Walter H.Eitner 21. The boundaries of a grammar - inter-dialectal reactions to positive anymore, William Labov 22. Dialect and grammar - data and theory
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".