Bibliographic record
Abstract
The main focus in chapters 4 and 5 is on studies employing the matched guise (and verbal guise) approaches. In this chapter we shall start by revisiting the study by Giles (1970), briefly introduced in chapter 3, to draw out some of its features and findings regarding UK attitudes to different accents of English. We shall then discuss advantages and disadvantages of this approach to studying language attitudes. We shall then review a number of studies that have investigated attitudes to native varieties of English in other English-speaking countries. This gives the chapter something of a native speaker English tone, with attention largely focused on ‘inner circle’ Englishes (‘inner circle’ Englishes is a term from Kachru 1985, 1988 referring to the Englishes of Australia, Canada, New Zealand, the UK, the USA, and other countries where English is said to have a traditional basis). In chapter 5, though, we will extend to more contexts. Research methods issues will continue to be picked out in context as we go along. UK: MORE DETAILS AND FINDINGS FROM GILES 1970 Giles' (1970) study presented accents of English to 177 secondary school students in South Wales and South-west England. The students were told they would be listening to different people, each reading in their own accent, although in reality the readings were all by the same person.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".