Dialect divergence and convergence in New Zealand English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recent research has been concerned with whether speech accommodation is an automatic process or determined by social factors (e.g. Trudgill 2008). This paper investigates phonetic accommodation in New Zealand English when speakers of NZE are responding to an Australian talker in a speech production task. NZ participants were randomly assigned to either a Positive or Negative group, where they were either flattered or insulted by the Australian. Overall, the NZE speakers accommodated to the speech of the AuE speaker. The flattery/insult manipulation did not influence degree of accommodation, but accommodation was predicted by participants' scores on an Implicit Association Task that measured Australia and New Zealand biases. Participants who scored with a pro-Australia bias were more likely to accommodate to the speech of the AuE speaker. Social biases about how a participant feels about a speaker predicted the extent of accommodation. These biases are, crucially, simultaneously automatic and social. (Speech accommodation, phonetic convergence, New Zealand English, dialect contact)*
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it