<scp>Elizabeth Gordon, Lyle Campbell, Jennifer Hay, Margaret Maclagan, Andrea Sudbury & Peter Trudgill</scp>, <i>New Zealand English: Its history and evolution</i>
Bibliographic record
Abstract
Elizabeth Gordon, Lyle Campbell, Jennifer Hay, Margaret Maclagan, Andrea Sudbury & Peter Trudgill, New Zealand English: Its history and evolution. Cambridge: Cambridge University Press, 2004. Pp. xix, 370. Hb $85.00. Between 1946 and 1948, Radio New Zealand established a Mobile Unit that visited many towns throughout the country, seeking out people who were longtime residents of small towns in order to record their oral histories. In 1986 Elizabeth Gordon was told of these archived recordings. The uniqueness of this data corpus is that the speakers, born between 1851 and 1904, were all participants in the formation of a new dialect, New Zealand English (NZE). It is unlikely that other data sets will be found in which tape recording technology and a first generation of speakers come together. English had arrived in 1840 with the original colonizers, who were mainly English, Scottish, and Irish. The story gets complicated by the arrival of many English-speaking immigrants from Australia, descendants from a penal colony founded in 1788, which had formed its own new dialect earlier with input from English, Scottish, and Irish settlers. Moreover, settlers often spent time first in Australia and then moved to New Zealand, and there was considerable contact between Australia and New Zealand from the start. It is within this historical setting and with this database that the Origins of New Zealand English (ONZE) project researchers set out to describe early NZE in order to examine its origins.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.175 | 0.078 |
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".